{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook presents the main part of the project. It is decomposed in the following parts:\n",
    "- Parameters setting \n",
    "- Creation of the trading environment \n",
    "- Set-up of the trading agent (actor)\n",
    "- Set-up of the portfolio vector memory (PVM)\n",
    "- Agent training \n",
    "- Agent Evaluation\n",
    "- Analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<u>Note:</u> This notebook has been cleaned up and run on a local machine. The appearing results are only for illustration and not representative of the project results in the presentation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BRBAcvyDeOBF"
   },
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-08T00:38:45.305452Z",
     "start_time": "2018-07-08T00:38:41.954075Z"
    },
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "1cA1tOpgeOBG",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Selim/anaconda/lib/python3.6/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from collections import deque\n",
    "import random\n",
    "import pandas as pd\n",
    "import ffn\n",
    "from environment import *\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xU0NVhPpd4y6"
   },
   "source": [
    "# Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-08T00:38:45.721074Z",
     "start_time": "2018-07-08T00:38:45.307419Z"
    }
   },
   "outputs": [],
   "source": [
    "# dataset\n",
    "\n",
    "#can be changed following the type of stocks studied \n",
    "\n",
    "path_data = './np_data/inputCrypto.npy'\n",
    "\n",
    "\n",
    "data_type = path_data.split('/')[2][5:].split('.')[0]\n",
    "namesBio=['JNJ','PFE','AMGN','MDT','CELG','LLY']\n",
    "namesUtilities=['XOM','CVX','MRK','SLB','MMM']\n",
    "namesTech=['FB','AMZN','MSFT','AAPL','T','VZ','CMCSA','IBM','CRM','INTC']\n",
    "namesCrypto = ['ETCBTC', 'ETHBTC', 'DOGEBTC', 'ETHUSDT', 'BTCUSDT', 'XRPBTC', 'DASHBTC', 'XMRBTC', 'LTCBTC', 'ETCETH']\n",
    "\n",
    "\n",
    "if data_type == 'Utilities':\n",
    "    list_stock = namesUtilities\n",
    "elif data_type == 'Bio':\n",
    "    list_stock = namesBio\n",
    "elif data_type == 'Tech':\n",
    "    list_stock = namesTech\n",
    "elif data_type == 'Crypto':\n",
    "    list_stock = namesCrypto\n",
    "else:\n",
    "    list_stock = [i for i in range(m)]\n",
    "\n",
    "\n",
    "# determine the length of the data, #features, #stocks\n",
    "data = np.load(path_data)\n",
    "trading_period = data.shape[2]\n",
    "nb_feature_map = data.shape[0]\n",
    "nb_stocks = data.shape[1]\n",
    "\n",
    "# fix parameters of the network\n",
    "m = nb_stocks\n",
    "\n",
    "###############################dictionaries of the problem###########################\n",
    "dict_hp_net = {'n_filter_1': 2, 'n_filter_2': 20, 'kernel1_size':(1, 3)}\n",
    "dict_hp_pb = {'batch_size': 50, 'ratio_train': 0.6,'ratio_val': 0.2, 'length_tensor': 10,\n",
    "              'ratio_greedy':0.8, 'ratio_regul': 0.1}\n",
    "dict_hp_opt = {'regularization': 1e-8, 'learning': 9e-2}\n",
    "dict_fin = {'trading_cost': 0.25/100, 'interest_rate': 0.02/250, 'cash_bias_init': 0.7}\n",
    "dict_train = {'pf_init_train': 10000, 'w_init_train': 'd', 'n_episodes':2, 'n_batches':10}\n",
    "dict_test = {'pf_init_test': 10000, 'w_init_test': 'd'}\n",
    "\n",
    "\n",
    "###############################HP of the network ###########################\n",
    "n_filter_1 = dict_hp_net['n_filter_1']\n",
    "n_filter_2 = dict_hp_net['n_filter_2']\n",
    "kernel1_size = dict_hp_net['kernel1_size']\n",
    "\n",
    "###############################HP of the problem###########################\n",
    "\n",
    "# Size of mini-batch during training\n",
    "batch_size = dict_hp_pb['batch_size']\n",
    "# Total number of steps for pre-training in the training set\n",
    "total_steps_train = int(dict_hp_pb['ratio_train']*trading_period)\n",
    "\n",
    "# Total number of steps for pre-training in the validation set\n",
    "total_steps_val = int(dict_hp_pb['ratio_val']*trading_period)\n",
    "\n",
    "# Total number of steps for the test\n",
    "total_steps_test = trading_period-total_steps_train-total_steps_val\n",
    "\n",
    "# Number of the columns (number of the trading periods) in each input price matrix\n",
    "n = dict_hp_pb['length_tensor']\n",
    "\n",
    "ratio_greedy = dict_hp_pb['ratio_greedy']\n",
    "\n",
    "ratio_regul = dict_hp_pb['ratio_regul']\n",
    "\n",
    "##############################HP of the optimization###########################\n",
    "\n",
    "\n",
    "# The L2 regularization coefficient applied to network training\n",
    "regularization = dict_hp_opt['regularization']\n",
    "# Parameter alpha (i.e. the step size) of the Adam optimization\n",
    "learning = dict_hp_opt['learning']\n",
    "\n",
    "optimizer = tf.train.AdamOptimizer(learning)\n",
    "\n",
    "\n",
    "##############################Finance parameters###########################\n",
    "\n",
    "trading_cost= dict_fin['trading_cost']\n",
    "interest_rate= dict_fin['interest_rate']\n",
    "cash_bias_init = dict_fin['cash_bias_init']\n",
    "\n",
    "############################## PVM Parameters ###########################\n",
    "sample_bias = 5e-5  # Beta in the geometric distribution for online training sample batches\n",
    "\n",
    "\n",
    "############################## Training Parameters ###########################\n",
    "\n",
    "w_init_train = np.array(np.array([1]+[0]*m))#dict_train['w_init_train']\n",
    "\n",
    "pf_init_train = dict_train['pf_init_train']\n",
    "\n",
    "n_episodes = dict_train['n_episodes']\n",
    "n_batches = dict_train['n_batches']\n",
    "\n",
    "############################## Test Parameters ###########################\n",
    "\n",
    "w_init_test = np.array(np.array([1]+[0]*m))#dict_test['w_init_test']\n",
    "\n",
    "pf_init_test = dict_test['pf_init_test']\n",
    "\n",
    "\n",
    "############################## other environment Parameters ###########################\n",
    "\n",
    "w_eq = np.array(np.array([1/(m+1)]*(m+1)))\n",
    "\n",
    "w_s = np.array(np.array([1]+[0.0]*m))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-08T00:38:45.733332Z",
     "start_time": "2018-07-08T00:38:45.723789Z"
    }
   },
   "outputs": [],
   "source": [
    "#random action function\n",
    "\n",
    "def get_random_action(m):\n",
    "    random_vec = np.random.rand(m+1)\n",
    "    return random_vec/np.sum(random_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-08T00:38:45.738422Z",
     "start_time": "2018-07-08T00:38:45.736043Z"
    }
   },
   "outputs": [],
   "source": [
    "#get_random_action(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Environment creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-08T00:38:45.794570Z",
     "start_time": "2018-07-08T00:38:45.740993Z"
    },
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "4UaHchSfeOBN"
   },
   "outputs": [],
   "source": [
    "#environment for trading of the agent \n",
    "# this is the agent trading environment (policy network agent)\n",
    "env = TradeEnv(path=path_data, window_length=n,\n",
    "               portfolio_value=pf_init_train, trading_cost=trading_cost,\n",
    "               interest_rate=interest_rate, train_size=dict_hp_pb['ratio_train'])\n",
    "\n",
    "\n",
    "#environment for equiweighted\n",
    "#this environment is set up for an agent who only plays an equiweithed portfolio (baseline)\n",
    "env_eq = TradeEnv(path=path_data, window_length=n,\n",
    "               portfolio_value=pf_init_train, trading_cost=trading_cost,\n",
    "               interest_rate=interest_rate, train_size=dict_hp_pb['ratio_train'])\n",
    "\n",
    "#environment secured (only money)\n",
    "#this environment is set up for an agentwho plays secure, keeps its money\n",
    "env_s = TradeEnv(path=path_data, window_length=n,\n",
    "               portfolio_value=pf_init_train, trading_cost=trading_cost,\n",
    "               interest_rate=interest_rate, train_size=dict_hp_pb['ratio_train'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-08T00:38:45.936744Z",
     "start_time": "2018-07-08T00:38:45.797252Z"
    }
   },
   "outputs": [],
   "source": [
    "#full on one stock environment \n",
    "#these environments are set up for agents who play only on one stock\n",
    "\n",
    "action_fu = list()\n",
    "env_fu = list()\n",
    "\n",
    "\n",
    "for i in range(m):\n",
    "    action = np.array([0]*(i+1) + [1] + [0]*(m-(i+1)))\n",
    "    action_fu.append(action)\n",
    "    \n",
    "    env_fu_i = TradeEnv(path=path_data, window_length=n,\n",
    "               portfolio_value=pf_init_train, trading_cost=trading_cost,\n",
    "               interest_rate=interest_rate, train_size=dict_hp_pb['ratio_train'])\n",
    "    \n",
    "    env_fu.append(env_fu_i)\n",
    "\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Definition of the Actor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-08T00:38:46.706796Z",
     "start_time": "2018-07-08T00:38:45.939668Z"
    },
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "aMEr2sNKeOBU"
   },
   "outputs": [],
   "source": [
    "# define neural net \\pi_\\phi(s) as a class\n",
    "class Policy(object):\n",
    "    '''\n",
    "    This class is used to instanciate the policy network agent\n",
    "\n",
    "    '''\n",
    "\n",
    "    def __init__(self, m, n, sess, optimizer,\n",
    "                 trading_cost=trading_cost,\n",
    "                 interest_rate=interest_rate,\n",
    "                 n_filter_1=n_filter_1,\n",
    "                 n_filter_2=n_filter_2):\n",
    "\n",
    "        # parameters\n",
    "        self.trading_cost = trading_cost\n",
    "        self.interest_rate = interest_rate\n",
    "        self.n_filter_1 = n_filter_1\n",
    "        self.n_filter_2 = n_filter_2\n",
    "        self.n = n\n",
    "        self.m = m\n",
    "\n",
    "        with tf.variable_scope(\"Inputs\"):\n",
    "\n",
    "            # Placeholder\n",
    "\n",
    "            # tensor of the prices\n",
    "            self.X_t = tf.placeholder(\n",
    "                tf.float32, [None, nb_feature_map, self.m, self.n])  # The Price tensor\n",
    "            # weights at the previous time step\n",
    "            self.W_previous = tf.placeholder(tf.float32, [None, self.m+1])\n",
    "            # portfolio value at the previous time step\n",
    "            self.pf_value_previous = tf.placeholder(tf.float32, [None, 1])\n",
    "            # vector of Open(t+1)/Open(t)\n",
    "            self.dailyReturn_t = tf.placeholder(tf.float32, [None, self.m])\n",
    "            \n",
    "            #self.pf_value_previous_eq = tf.placeholder(tf.float32, [None, 1])\n",
    "            \n",
    "            \n",
    "\n",
    "        with tf.variable_scope(\"Policy_Model\"):\n",
    "\n",
    "            # variable of the cash bias\n",
    "            bias = tf.get_variable('cash_bias', shape=[\n",
    "                                   1, 1, 1, 1], initializer=tf.constant_initializer(cash_bias_init))\n",
    "            # shape of the tensor == batchsize\n",
    "            shape_X_t = tf.shape(self.X_t)[0]\n",
    "            # trick to get a \"tensor size\" for the cash bias\n",
    "            self.cash_bias = tf.tile(bias, tf.stack([shape_X_t, 1, 1, 1]))\n",
    "            # print(self.cash_bias.shape)\n",
    "\n",
    "            with tf.variable_scope(\"Conv1\"):\n",
    "                # first layer on the X_t tensor\n",
    "                # return a tensor of depth 2\n",
    "                self.conv1 = tf.layers.conv2d(\n",
    "                    inputs=tf.transpose(self.X_t, perm=[0, 3, 2, 1]),\n",
    "                    activation=tf.nn.relu,\n",
    "                    filters=self.n_filter_1,\n",
    "                    strides=(1, 1),\n",
    "                    kernel_size=kernel1_size,\n",
    "                    padding='same')\n",
    "\n",
    "            with tf.variable_scope(\"Conv2\"):\n",
    "                \n",
    "                #feature maps\n",
    "                self.conv2 = tf.layers.conv2d(\n",
    "                    inputs=self.conv1,\n",
    "                    activation=tf.nn.relu,\n",
    "                    filters=self.n_filter_2,\n",
    "                    strides=(self.n, 1),\n",
    "                    kernel_size=(1, self.n),\n",
    "                    padding='same')\n",
    "\n",
    "            with tf.variable_scope(\"Tensor3\"):\n",
    "                #w from last periods\n",
    "                # trick to have good dimensions\n",
    "                w_wo_c = self.W_previous[:, 1:]\n",
    "                w_wo_c = tf.expand_dims(w_wo_c, 1)\n",
    "                w_wo_c = tf.expand_dims(w_wo_c, -1)\n",
    "                self.tensor3 = tf.concat([self.conv2, w_wo_c], axis=3)\n",
    "\n",
    "            with tf.variable_scope(\"Conv3\"):\n",
    "                #last feature map WITHOUT cash bias\n",
    "                self.conv3 = tf.layers.conv2d(\n",
    "                    inputs=self.conv2,\n",
    "                    activation=tf.nn.relu,\n",
    "                    filters=1,\n",
    "                    strides=(self.n_filter_2 + 1, 1),\n",
    "                    kernel_size=(1, 1),\n",
    "                    padding='same')\n",
    "\n",
    "            with tf.variable_scope(\"Tensor4\"):\n",
    "                #last feature map WITH cash bias\n",
    "                self.tensor4 = tf.concat([self.cash_bias, self.conv3], axis=2)\n",
    "                # we squeeze to reduce and get the good dimension\n",
    "                self.squeezed_tensor4 = tf.squeeze(self.tensor4, [1, 3])\n",
    "\n",
    "            with tf.variable_scope(\"Policy_Output\"):\n",
    "                # softmax layer to obtain weights\n",
    "                self.action = tf.nn.softmax(self.squeezed_tensor4)\n",
    "\n",
    "            with tf.variable_scope(\"Reward\"):\n",
    "                # computation of the reward\n",
    "                #please look at the chronological map to understand\n",
    "                constant_return = tf.constant(\n",
    "                    1+self.interest_rate, shape=[1, 1])\n",
    "                cash_return = tf.tile(\n",
    "                    constant_return, tf.stack([shape_X_t, 1]))\n",
    "                y_t = tf.concat(\n",
    "                    [cash_return, self.dailyReturn_t], axis=1)\n",
    "                Vprime_t = self.action * self.pf_value_previous\n",
    "                Vprevious = self.W_previous*self.pf_value_previous\n",
    "\n",
    "                # this is just a trick to get the good shape for cost\n",
    "                constant = tf.constant(1.0, shape=[1])\n",
    "\n",
    "                cost = self.trading_cost * \\\n",
    "                    tf.norm(Vprime_t-Vprevious, ord=1, axis=1)*constant\n",
    "\n",
    "                cost = tf.expand_dims(cost, 1)\n",
    "\n",
    "                zero = tf.constant(\n",
    "                    np.array([0.0]*m).reshape(1, m), shape=[1, m], dtype=tf.float32)\n",
    "\n",
    "                vec_zero = tf.tile(zero, tf.stack([shape_X_t, 1]))\n",
    "                vec_cost = tf.concat([cost, vec_zero], axis=1)\n",
    "\n",
    "                Vsecond_t = Vprime_t - vec_cost\n",
    "\n",
    "                V_t = tf.multiply(Vsecond_t, y_t)\n",
    "                self.portfolioValue = tf.norm(V_t, ord=1)\n",
    "                self.instantaneous_reward = (\n",
    "                    self.portfolioValue-self.pf_value_previous)/self.pf_value_previous\n",
    "                \n",
    "                \n",
    "            with tf.variable_scope(\"Reward_Equiweighted\"):\n",
    "                constant_return = tf.constant(\n",
    "                    1+self.interest_rate, shape=[1, 1])\n",
    "                cash_return = tf.tile(\n",
    "                    constant_return, tf.stack([shape_X_t, 1]))\n",
    "                y_t = tf.concat(\n",
    "                    [cash_return, self.dailyReturn_t], axis=1)\n",
    "  \n",
    "\n",
    "                V_eq = w_eq*self.pf_value_previous\n",
    "                V_eq_second = tf.multiply(V_eq, y_t)\n",
    "        \n",
    "                self.portfolioValue_eq = tf.norm(V_eq_second, ord=1)\n",
    "            \n",
    "                self.instantaneous_reward_eq = (\n",
    "                    self.portfolioValue_eq-self.pf_value_previous)/self.pf_value_previous\n",
    "                \n",
    "            with tf.variable_scope(\"Max_weight\"):\n",
    "                self.max_weight = tf.reduce_max(self.action)\n",
    "                print(self.max_weight.shape)\n",
    "\n",
    "                \n",
    "            with tf.variable_scope(\"Reward_adjusted\"):\n",
    "                \n",
    "                self.adjested_reward = self.instantaneous_reward - self.instantaneous_reward_eq - ratio_regul*self.max_weight\n",
    "                \n",
    "        #objective function \n",
    "        #maximize reward over the batch \n",
    "        # min(-r) = max(r)\n",
    "        self.train_op = optimizer.minimize(-self.adjested_reward)\n",
    "        \n",
    "        # some bookkeeping\n",
    "        self.optimizer = optimizer\n",
    "        self.sess = sess\n",
    "\n",
    "    def compute_W(self, X_t_, W_previous_):\n",
    "        \"\"\"\n",
    "        This function returns the action the agent takes \n",
    "        given the input tensor and the W_previous\n",
    "        \n",
    "        It is a vector of weight\n",
    "\n",
    "        \"\"\"\n",
    "\n",
    "        return self.sess.run(tf.squeeze(self.action), feed_dict={self.X_t: X_t_, self.W_previous: W_previous_})\n",
    "\n",
    "    def train(self, X_t_, W_previous_, pf_value_previous_, dailyReturn_t_):\n",
    "        \"\"\"\n",
    "        This function trains the neural network\n",
    "        maximizing the reward \n",
    "        the input is a batch of the differents values\n",
    "        \"\"\"\n",
    "        self.sess.run(self.train_op, feed_dict={self.X_t: X_t_,\n",
    "                                                self.W_previous: W_previous_,\n",
    "                                                self.pf_value_previous: pf_value_previous_,\n",
    "                                                self.dailyReturn_t: dailyReturn_t_})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Definition of the PVM Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-08T00:38:46.746472Z",
     "start_time": "2018-07-08T00:38:46.709092Z"
    },
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "p959bcH_AJdJ"
   },
   "outputs": [],
   "source": [
    "class PVM(object):\n",
    "    '''\n",
    "    This is the memory stack called PVM in the paper\n",
    "    '''\n",
    "\n",
    "    def __init__(self, m, sample_bias, total_steps = total_steps_train, \n",
    "                 batch_size = batch_size, w_init = w_init_train):\n",
    "        \n",
    "        \n",
    "        #initialization of the memory \n",
    "        #we have a total_step_times the initialization portfolio tensor \n",
    "        self.memory = np.transpose(np.array([w_init]*total_steps))  \n",
    "        self.sample_bias = sample_bias\n",
    "        self.total_steps = total_steps\n",
    "        self.batch_size = batch_size\n",
    "\n",
    "    def get_W(self, t):\n",
    "        #return the weight from the PVM at time t \n",
    "        return self.memory[:, t]\n",
    "\n",
    "    def update(self, t, w):\n",
    "        #update the weight at time t\n",
    "        self.memory[:, t] = w\n",
    "\n",
    "\n",
    "    def draw(self, beta=sample_bias):\n",
    "        '''\n",
    "        returns a valid step so you can get a training batch starting at this step\n",
    "        '''\n",
    "        while 1:\n",
    "            z = np.random.geometric(p=beta)\n",
    "            tb = self.total_steps - self.batch_size + 1 - z\n",
    "            if tb >= 0:\n",
    "                return tb\n",
    "            \n",
    "    def test(self):\n",
    "        #just to test\n",
    "        return self.memory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AUT4gLhveOBW"
   },
   "source": [
    "Try to rollout trajecories using the policy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-08T00:38:46.753814Z",
     "start_time": "2018-07-08T00:38:46.748415Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_max_draw_down(xs):\n",
    "    xs = np.array(xs)\n",
    "    i = np.argmax(np.maximum.accumulate(xs) - xs) # end of the period\n",
    "    j = np.argmax(xs[:i]) # start of period\n",
    "    \n",
    "    return xs[j] - xs[i]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-08T00:38:46.960918Z",
     "start_time": "2018-07-08T00:38:46.756178Z"
    }
   },
   "outputs": [],
   "source": [
    "def eval_perf(e):\n",
    "    \"\"\"\n",
    "    This function evaluates the performance of the different types of agents. \n",
    "    \n",
    "    \n",
    "    \"\"\"\n",
    "    list_weight_end_val = list()\n",
    "    list_pf_end_training = list()\n",
    "    list_pf_min_training = list()\n",
    "    list_pf_max_training = list()\n",
    "    list_pf_mean_training = list()\n",
    "    list_pf_dd_training = list()\n",
    "    \n",
    "    #######TEST#######\n",
    "    #environment for trading of the agent \n",
    "    env_eval = TradeEnv(path=path_data, window_length=n,\n",
    "                   portfolio_value=pf_init_train, trading_cost=trading_cost,\n",
    "                   interest_rate=interest_rate, train_size=dict_hp_pb['ratio_train'])\n",
    "\n",
    "\n",
    "\n",
    "    #initialization of the environment \n",
    "    state_eval, done_eval = env_eval.reset(w_init_test, pf_init_test, t = total_steps_train)\n",
    "\n",
    "\n",
    "\n",
    "    #first element of the weight and portfolio value \n",
    "    p_list_eval = [pf_init_test]\n",
    "    w_list_eval = [w_init_test]\n",
    "\n",
    "    for k in range(total_steps_train, total_steps_train +total_steps_val-int(n/2)):\n",
    "        X_t = state_eval[0].reshape([-1]+ list(state_eval[0].shape))\n",
    "        W_previous = state_eval[1].reshape([-1]+ list(state_eval[1].shape))\n",
    "        pf_value_previous = state_eval[2]\n",
    "        #compute the action \n",
    "        action = actor.compute_W(X_t, W_previous)\n",
    "        #step forward environment \n",
    "        state_eval, reward_eval, done_eval = env_eval.step(action)\n",
    "\n",
    "        X_next = state_eval[0]\n",
    "        W_t_eval = state_eval[1]\n",
    "        pf_value_t_eval = state_eval[2]\n",
    "\n",
    "        dailyReturn_t = X_next[-1, :, -1]\n",
    "        #print('current portfolio value', round(pf_value_previous,0))\n",
    "        #print('weights', W_previous)\n",
    "        p_list_eval.append(pf_value_t_eval)\n",
    "        w_list_eval.append(W_t_eval)\n",
    "        \n",
    "    list_weight_end_val.append(w_list_eval[-1])\n",
    "    list_pf_end_training.append(p_list_eval[-1])\n",
    "    list_pf_min_training.append(np.min(p_list_eval))\n",
    "    list_pf_max_training.append(np.max(p_list_eval))\n",
    "    list_pf_mean_training.append(np.mean(p_list_eval))\n",
    "    \n",
    "    list_pf_dd_training.append(get_max_draw_down(p_list_eval))\n",
    "\n",
    "    print('End of test PF value:',round(p_list_eval[-1]))\n",
    "    print('Min of test PF value:',round(np.min(p_list_eval)))\n",
    "    print('Max of test PF value:',round(np.max(p_list_eval)))\n",
    "    print('Mean of test PF value:',round(np.mean(p_list_eval)))\n",
    "    print('Max Draw Down of test PF value:',round(get_max_draw_down(p_list_eval)))\n",
    "    print('End of test weights:',w_list_eval[-1])\n",
    "    plt.title('Portfolio evolution (validation set) episode {}'.format(e))\n",
    "    plt.plot(p_list_eval, label = 'Agent Portfolio Value')\n",
    "    plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "    plt.show()\n",
    "    plt.title('Portfolio weights (end of validation set) episode {}'.format(e))\n",
    "    plt.bar(np.arange(m+1), list_weight_end_val[-1])\n",
    "    plt.xticks(np.arange(m+1), ['Money'] + list_stock, rotation=45)\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "    names = ['Money'] + list_stock\n",
    "    w_list_eval = np.array(w_list_eval)\n",
    "    for j in range(m+1):\n",
    "        plt.plot(w_list_eval[:,j], label = 'Weight Stock {}'.format(names[j]))\n",
    "        plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.5)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RL Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-08T02:30:27.617009Z",
     "start_time": "2018-07-08T00:38:46.963263Z"
    },
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "hNxzXPFleOBi",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "()\n",
      "Start Episode 0\n",
      "End of test PF value: 34759.0\n",
      "Min of test PF value: 9752.0\n",
      "Max of test PF value: 35586.0\n",
      "Mean of test PF value: 20179.0\n",
      "Max Draw Down of test PF value: 3682.0\n",
      "End of test weights: [0.16736464 0.08357985 0.08351066 0.08492032 0.08350961 0.08270566\n",
      " 0.0830933  0.08294203 0.08338225 0.08187818 0.0831135 ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a26218978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FRgEvFImwEEKI5qOWdSajgDnu/hSAmU0FxgOPVTy4+9Pp2uLsjUnptLj7zcnfwnKiLYQQopmoxcw1AJibOZ+X3GphI+AVM7vWzB40s9NST2cpzOxQM5tpZjMXLFhQY9BCCCGahVqUiVVx8xrDbwFGA98CtgHWI8xhSwfmfr67j3T3ka2trTUGLYQQolmoRZnMAwZlzgcC82sMfx7woLs/5e6LgF8DWy1bFIUQQjQ7tSiTGcCGZjbMzHoBE4BpNYY/A1jdzCrdjZ3IjLUIIYRYPuhQmaQexSTgJmA2cLW7zzKzE81sLwAz28bM5gH7AOeZ2ax077uEietWM3uEMJldUJ+kCCGE6Cpq2jXY3acD03NuUzL/ZxDmr2r33gxsXiCOQgghmhytgBdCCFEYKRMhhBCFkTIRQghRGCkTIYQQhZEyEUIIURgpEyGEEIWRMhFCCFEYKRMhhBCFkTIRQghRGCkTIYQQhZEyEUIIURgpEyGEEIWRMhFCCFEYKRMhhBCFkTIRQghRmJqUiZmNNbMnzGyOmU2ucn17M3vAzBaZ2d5Vrq9mZs+Z2VllRFoIIURz0aEyMbOewNnAOGA4sK+ZDc95exaYCFzRRjDfB+7ofDSFEEI0M7X0TEYBc9z9KXd/G5gKjM96cPen3f1hYHH+ZjPbGlgH+H0J8RVCCNGE1KJMBgBzM+fzkluHmFkP4CfAt5c9akIIIboLtSgTq+LmNYZ/ODDd3ee258nMDjWzmWY2c8GCBTUGLYQQolloqcHPPGBQ5nwgML/G8D8GjDazw4FVgF5mttDdlxrEd/fzgfMBRo4cWauiEkII0STUokxmABua2TDgOWACsF8tgbv7/pX/ZjYRGJlXJEIIIbo/HZq53H0RMAm4CZgNXO3us8zsRDPbC8DMtjGzecA+wHlmNquekRZCCNFc1NIzwd2nA9NzblMy/2cQ5q/2wrgYuHiZYyiEEKLp0Qp4IYQQhZEyEUIIURgpEyGEEIWRMhFCCFEYKRMhhBCFkTIRQghRGCkTIYQQhZEyEUIIURgpEyGEEIWRMhFCCFEYKRMhhBCFkTIRQghRGCkTIYQQhZEyEUIIURgpEyGEEIWRMhFCCFGYmpSJmY01syfMbI6Zve+zu2a2vZk9YGaLzGzvjPsWZna3mc0ys4fN7PNlRl4IIURz0KEyMbOewNnAOGA4sK+ZDc95exaYCFyRc38TOMDdRwBjgTPMrF/RSAshhGguavls7yhgjrs/BWBmU4HxwGMVD+7+dLq2OHuju/8183++mb0AtAKvFI65EEKIpqEWM9cAYG7mfF5yWybMbBTQC3iyyrVDzWymmc1csGDBsgYthBCii6lFmVgVN18WIWa2LnAZcJC7L85fd/fz3X2ku49sbW1dlqCFEEI0AbUok3nAoMz5QGB+rQLMbDXgBuAYd79n2aInhBCiO1CLMpkBbGhmw8ysFzABmFZL4Mn/dcCl7v6rzkdTCCFEM9OhMnH3RcAk4CZgNnC1u88ysxPNbC8AM9vGzOYB+wDnmdmsdPvngO2BiWb2UDq2qEtKhBBCdBm1zObC3acD03NuUzL/ZxDmr/x9lwOXF4yjEEKIJkcr4IUQQhRGykQIIURhpEyEEEIURspECCFEYaRMhBBCFEbKRAghRGGkTIQQQhRGykQIIURhpEyEEEIURspECCFEYaRMhBBCFEbKRAghRGGkTIQQQhRGykQIIURhpEyEEEIURspECCFEYWpSJmY21syeMLM5Zja5yvXtzewBM1tkZnvnrh1oZn9Lx4FlRVwIIUTz0KEyMbOewNnAOGA4sK+ZDc95exaYCFyRu3cN4DhgW2AUcJyZrV482kIIIZqJWnomo4A57v6Uu78NTAXGZz24+9Pu/jCwOHfvbsDN7v6Su78M3AyMLSHeQgghmohalMkAYG7mfF5yq4Wa7jWzQ81sppnNXLBgQY1BCyGEaBZqUSZWxc1rDL+me939fHcf6e4jW1tbawxaCCFEs1CLMpkHDMqcDwTm1xh+kXuFEEJ0E2pRJjOADc1smJn1AiYA02oM/yZgVzNbPQ2875rchBBCLEd0qEzcfREwiVACs4Gr3X2WmZ1oZnsBmNk2ZjYP2Ac4z8xmpXtfAr5PKKQZwInJTQghxHJESy2e3H06MD3nNiXzfwZhwqp270XARQXiKIQQosnRCnghhBCFkTIRQghRGCkTIYQQhZEyEUIIURgpEyGEEIWRMhFCCFEYKRMhhBCFkTIRQghRGCkTIYQQhZEyEUIIURgpEyGEEIWRMhFCCFEYKRMhhBCFqWnXYPHBZejkG+oW9tM//FTdwhZCNBYpk4LUq7JVRSuE6E5ImXQzlveewvKePmh8A2R5kdeezEayvKevs9SkTMxsLHAm0BP4ubv/MHe9N3ApsDXwL+Dz7v60ma0A/BzYKsm61N1PKTH+QhRGvUshitOhMjGznsDZwCeBecAMM5vm7o9lvB0MvOzuG5jZBOBU4PPEZ3x7u/tmZrYy8JiZXenuT5edECHEBxP1FJqDWmZzjQLmuPtT7v42MBUYn/MzHrgk/b8G2NnMDHCgj5m1ACsBbwOvlRJzIYQQTUMtymQAMDdzPi+5VfXj7ouAV4E1CcXyBvAP4Fngx+7+Ul6AmR1qZjPNbOaCBQuWORFCCCG6llqUiVVx8xr9jALeBfoDw4AjzWy993l0P9/dR7r7yNbW1hqiJIQQopmoRZnMAwZlzgcC89vyk0xafYGXgP2A37n7O+7+AvAnYGTRSAshhGgualEmM4ANzWyYmfUCJgDTcn6mAQem/3sDt7m7E6atnSzoA3wUeLycqAshhGgWOlQmaQxkEnATMBu42t1nmdmJZrZX8nYhsKaZzQGOACYn97OBVYBHCaX0C3d/uOQ0CCGE6GJqWmfi7tOB6Tm3KZn/bxHTgPP3LazmLoQQYvlCGz0KIYQojJSJEEKIwkiZCCGEKIyUiRBCiMJImQghhCiMlIkQQojCSJkIIYQojJSJEEKIwkiZCCGEKIyUiRBCiMJImQghhCiMlIkQQojCSJkIIYQojJSJEEKIwkiZCCGEKExNysTMxprZE2Y2x8wmV7ne28yuStfvNbOhmWubm9ndZjbLzB4xsxXLi74QQohmoENlYmY9iS8mjgOGA/ua2fCct4OBl919A+B04NR0bwtwOXCYu48AdgDeKS32QgghmoJaeiajgDnu/pS7vw1MBcbn/IwHLkn/rwF2NjMDdgUedve/ALj7v9z93XKiLoQQolmoRZkMAOZmzuclt6p+0jfjXwXWBDYC3MxuMrMHzOyo4lEWQgjRbNTyDXir4uY1+mkBPgFsA7wJ3Gpm97v7rUvdbHYocCjA4MGDa4iSEEKIZqKWnsk8YFDmfCAwvy0/aZykL/BScr/D3V909zeB6cBWeQHufr67j3T3ka2trcueCiGEEF1KLcpkBrChmQ0zs17ABGBazs804MD0f2/gNnd34CZgczNbOSmZMcBj5URdCCFEs9ChmcvdF5nZJEIx9AQucvdZZnYiMNPdpwEXApeZ2RyiRzIh3fuymf2UUEgOTHf3G+qUFiGEEF1ELWMmuPt0wkSVdZuS+f8WsE8b915OTA8WQgixnKIV8EIIIQojZSKEEKIwUiZCCCEKI2UihBCiMFImQgghCiNlIoQQojBSJkIIIQojZSKEEKIwUiZCCCEKI2UihBCiMFImQgghCiNlIoQQojBSJkIIIQojZSKEEKIwUiZCCCEKI2UihBCiMDUpEzMba2ZPmNkcM5tc5XpvM7sqXb/XzIbmrg82s4Vm9q1yoi2EEKKZ6FCZmFlP4GxgHDAc2NfMhue8HQy87O4bAKcDp+aunw7cWDy6QgghmpFaeiajgDnu/pS7vw1MBcbn/IwHLkn/rwF2NjMDMLNPA08Bs8qJshBCiGajFmUyAJibOZ+X3Kr6cfdFwKvAmmbWB/gOcEJ7AszsUDObaWYzFyxYUGvchRBCNAm1KBOr4uY1+jkBON3dF7YnwN3Pd/eR7j6ytbW1higJIYRoJlpq8DMPGJQ5HwjMb8PPPDNrAfoCLwHbAnub2Y+AfsBiM3vL3c8qHHMhhBBNQy3KZAawoZkNA54DJgD75fxMAw4E7gb2Bm5zdwdGVzyY2fHAQikSIYRY/uhQmbj7IjObBNwE9AQucvdZZnYiMNPdpwEXApeZ2RyiRzKhnpEWQgjRXNTSM8HdpwPTc25TMv/fAvbpIIzjOxE/IYQQ3QCtgBdCCFEYKRMhhBCFkTIRQghRGCkTIYQQhZEyEUIIURgpEyGEEIWRMhFCCFEYKRMhhBCFkTIRQghRGCkTIYQQhZEyEUIIURgpEyGEEIWRMhFCCFEYKRMhhBCFkTIRQghRGCkTIYQQhalJmZjZWDN7wszmmNnkKtd7m9lV6fq9ZjY0uX/SzO43s0fS707lRl8IIUQz0KEyMbOewNnAOGA4sK+ZDc95Oxh42d03AE4HTk3uLwJ7uvtmxDfiLysr4kIIIZqHWnomo4A57v6Uu78NTAXG5/yMBy5J/68BdjYzc/cH3X1+cp8FrGhmvcuIuBBCiOahFmUyAJibOZ+X3Kr6cfdFwKvAmjk/nwUedPf/5AWY2aFmNtPMZi5YsKDWuAshhGgSalEmVsXNl8WPmY0gTF9fribA3c9395HuPrK1tbWGKAkhhGgmalEm84BBmfOBwPy2/JhZC9AXeCmdDwSuAw5w9yeLRlgIIUTzUYsymQFsaGbDzKwXMAGYlvMzjRhgB9gbuM3d3cz6ATcA33X3P5UVaSGEEM1Fh8okjYFMAm4CZgNXu/ssMzvRzPZK3i4E1jSzOcARQGX68CRgA+BYM3soHWuXngohhBBdSkstntx9OjA95zYl8/8tYJ8q950EnFQwjkIIIZocrYAXQghRGCkTIYQQhZEyEUIIURgpEyGEEIWRMhFCCFEYKRMhhBCFkTIRQghRGCkTIYQQhZEyEUIIURgpEyGEEIWRMhFCCFEYKRMhhBCFkTIRQghRGCkTIYQQhZEyEUIIURgpEyGEEIWpSZmY2Vgze8LM5pjZ5CrXe5vZVen6vWY2NHPtu8n9CTPbrbyoCyGEaBY6VCZm1hM4GxgHDAf2NbPhOW8HAy+7+wbA6cCp6d7hxDfjRwBjgZ+l8IQQQixH1NIzGQXMcfen3P1tYCowPudnPHBJ+n8NsLOZWXKf6u7/cfe/A3NSeEIIIZYjzN3b92C2NzDW3Q9J518EtnX3SRk/jyY/89L5k8C2wPHAPe5+eXK/ELjR3a/JyTgUODSdbgw8UTxpNbEW8GKDZEne8iFT8rq3vK6Q2Sh5Q9y9tQFyqtJSgx+r4pbXQG35qeVe3P184Pwa4lIqZjbT3UdKXveU1xUyJa97y+sKmV2Rxq6gFjPXPGBQ5nwgML8tP2bWAvQFXqrxXiGEEN2cWpTJDGBDMxtmZr2IAfVpOT/TgAPT/72B2zzsZ9OACWm21zBgQ+C+cqIuhBCiWejQzOXui8xsEnAT0BO4yN1nmdmJwEx3nwZcCFxmZnOIHsmEdO8sM7saeAxYBPyPu79bp7R0hkab1iSv+8uUvO4trytkdkUaG06HA/BCCCFER2gFvBBCiMJImQghhCiMlMkHHDNbsYvlr9BgeQ3dgaHe8pog//qYWd3rkUbmm5n1bpSs5QkpkybCzDYzs03MbLUGydsFOMXMNmmEvCrydwS+YmabNUjensCURlVM9ZbXBPk3ltg66dNpSUC95OwATDSzzeslIyNrV+DY9NsQzGy75WGbKSmTdjCzEWb2pQbJ2h24FNgGWLtB8s4GZgKr1lteG/J/BjxHbYtni8rbFTgR+H0jZhTWW14T5N+ewI+A64E/ufuiOsn5FPGevAIsroeMjKw9gZ8A9wCP11NWjq8CTzSih1dX3F1H7iBW7vcAvgD8qAHydiL2LduuQelbH3gUGNNFz/fDRGH9WIPk7UZsZ3Fmxq1Hd5XXBPk3GLgX2CadV2aFlvpMgS2IrZW2bUCaNgD+AozMuVuDnum5wP31fC/rfXRvTVg/Wtx9MfBnYKyZfboeQtJmmAC7AKe6+58r3d3MtXrQE/ibu9/RAFnVWAO4393vrndrLLU2TyEK63/M7DAAd19cD9kNktfV+fc28DzwiJnTlvIMAAAXkUlEQVT18lQbpjJDWtxcBusQvZ5762lGSxixoe3MbPwraStdmFmrmWV7lEcQjay7uqvJS8okR7I/n2Rmm7v7U8AxwOZmtkIdKp+B6Xddlph6KgXTU3w2M7NVyhBmZqPN7LPA68CqZjakIiujxMaZ2Ygy5FWRv42ZbU0sYH0vvZZIfnYqwzZuZj3S4P5+wDfc/RjgQWALM/syvFfBl1IRN0JeE+TfLmb2eWB1whTb293fTmmv5N96wF4F0znSzDYAVgD6wHuLp7PvycdLek92MLOdiS2g1k6y8mkakcaHSiGl7WTisx6Y2YeA3wFfJhZ4398dTV7dLsL1IPfi9wEWABeb2ZFEr2EzoG/Jlc/uwM2pErgD2NPMhiQZLRk5OwGFdwJNheH/gLnELgWvAbtU5PgSu/4IYgucUluCSf7FwFuESe8TZnZQplVbaQEOB7YpoTCt4O7vuPu+7n5ncrsR+AOwZaaC95LytK7ymiD/dgX+F5jr7rMJM9uvzGzV1COp5NcniR3DOyXf4gN6VxC9r3uI9+RL8N47Unl2WwIjCiqtXYke5GvuPhNYaGaXJFnZNI0Gtiqjx2BmY4CtgCeBnczsv4ltp6a6+8Xu/iXgEeDv3U6hdLWdrasPlth7twe+RhSGnsCmwB7AtURL8HSgZ0kydwNmkcYMgGHAacAJxDbSFX/7EgWqfwnyXiAzRgHsStiIDwE2TW4HpHhtXPIzrsjfOuO2I/BP4KBceh8BNipB3uXAbcBviBbg6ulaX+DzxOD110tMX93kNUn+vUZsh1RxWwM4J6V5HaBfSueDwIgCcv5BZuwwpfMu4NCM2+dT2jcsmKZXgAkZt6HAlcAvM/XCPsADlWdc8DnuCsyulAPgK0RD8vwqfs8H1iszH+t9dHkEmuEAPpVezknA3cBRmWu9gf2B84CVS5C1a6pErwaG5dx/BNwKHAkcSww+friktD0DnJS7tgvRCryLUJoPFpVXRf5YohX7CPDD3LUdgaeB64BfpYqwaHorivozwEjgh0SP6BtE7xJi9tMBwE+Bfs0sr0ny72FCGU4FPp7cLaXrHOD2lId3ApsXeI5PpLC+m3FfEdiZ6JH9P6KyfwzYrGCePUo0AH4ObJDcWwjT8/VEj/JaYmPaTqWpisx/k2k8JfdDCMWxF7BamXnX6KPLI9DVB7AScCbQP1VuDwDrpmurZfz9CdiloKxdgb8CE4GjgB8AH81cH0BskvkT4Ghgk4LyBgM3AFum83uB/835WYUwo60HrFXysx2entt2qaD+DvhZzs+6wObARyneAxsNPAXsnHOfCFwG7JhL96rNLK8J8m+tVKlWFMhXU0X7sZy/gURPZfVOyvkIoTB3JD5ZcSkxISXrZ23CerA9MLBAmgYlBfEJQlEdB1wFrJ/ztzEwBGgt4TnuRijkC4A/klNOwP8QSm0foE+ZedjIo8sj0AwH0SOYRszeGpzcxqXKYoVUWOZQsNuZKszt0v/NCGVyEnWY+gisSQyUDsy49ctXSNRpKiIx9tSbjMkqFeQbgbPrJPMI4NfErJjeuWtnAtd0F3lNkH/9U/71zbi1porvtyxtcut0HJKMFpLJCuhF9PAuIdeTLSFNa2Z/0/+hwBSi11W6WYloKF0KfCKdf4tosI7I+TsSOAtYpR752YijyyPQ8AQvsYUOqLRGiK78PcCB6Xw7ogfxicx9axSQuQNwGLBfzn0EMavjJHLz2wumcSyxmO2MVPh7AL3Stb6pQjq9js94d6IXcjrJHEEabyIU843kWtgF5W1GtOKNMA/+jJi4sELGz5iylEm95TVB/u2RZNwHHJK7VlEovwZGl/CeXEMMgmcr+J7A1kmhnFxSmvYkxifuBD6Zu1ZRKL8kmbxKkvlJwjy+STqv1D1HtKFQOtWza5ajyyPQJYmOF+sRYuHcd1IBOQKYTthlHwX2yL4ABWRVTFtfJ8ZK8grlw8RK6dOBrUpI2+7EuM9ewEHVKh1gNcI+XWrLL4U9lrDdjwUuAv6vip8BRC/wtBLkjUv5+Ml03oOYyHBOtoInJldckCqqTudpveU1Uf59hGhUPQCsk/PTSrSwpxKmomV+nknOX4hxn2uBC3PXVyBmPV0LHFdCmh4gFNRB6fn2yfkZSox3XUisMyv6HHcls7CU9/dcjyAU9kfKzsOuOro8Ag1PMGxC2H03JmZRzUwFfyWia/9Rci2JArJ2SwXmo+n8EGIGx045fx8h1rMUss8SK6NfA76Wzrcitis5iZhWmm05rwYMLfnZbgY8S2rNEl38u4HvEbsJ9Mv47U8yKRaQN4aYHZMfszCikXA+oawPIhoPw5tZHjHu0ZX5NzTF+8h03o9oCE0lxvBGZ/wWGSOpTI3dL51/mOjJfo9QyGsk9xZiFXynx9JSOf8rcFg6XyeVyQtTedws43cQJYw7VSn3g4ieT2vO3zHEhINeNGilfT2PD9THsdLioOOIF3S8u79gZhsT3enfu/uUkuWdAnzO3dc3szWIgnoDYfa6Hpjs7m8nv70q/zspq8VjYdcUolK6lmg93ka8sAcBKxPTKs1L3i8qrRoeTEwsuJtQKqcS6f03Yd6aC/wYWOwlvHhm9vUU1v+Z2ZqE2XBb4Hl3v8zMvkmYbNYF9nb3x5pZnpmtBHwT2IiYHXUkjcu/vsQU+JOBd4gB428SPfW7gM8RSnOKu79ZUNZo4t08H/gXYSa8gTRGQwxSX170HbHY/bcX0eufRTQEphDTt/9K9IrmE1smvVNEVk7uKcA+7r6BmbUSZt0L3f2cKn7XcPeXypLdpXS1Nqv3QU7jEzNGriJ6Ix9KbpsQXftS7KVEi/zD6f+5xHjMvSxpsa9NTG/8UknyNiIK/JpEgf82UUh/mvGzCWF26l2GzJz8sSQzRXq+pxMtz5Mzfg4CzilJXmXK7b6EyWk3QjlfTXxeejppTIaY7r1+M8sjFNKhhBlmQ+BwYmHi6Rk/9cy/9YjxmY8TvYETUnk4Iyf/dnImr2WU05L5vzsxrXk2cEzG/SjgghLStBuxyHIrYlbaueSmp6d39feUNIOqSrm/O5X7L+X8FeqRN+vRvVZYLiNmZu7uZrarmR1rZicTZq2fE4X2s2a2rrs/Tkx/nFOCzHFEIfmYma3v7ocRg35ruPvPAdz9heSnrG95PEW0JK8mTA+nEYsgB6ftSyAqik2I1l9ppBXLPyDm/+PufyAGHW8CFqSeX4UhFt+/KLJqeShwVWrdXkdUsKcSs+1+QIxpfJtoxePuZ7n7k80qLz2/XxDm1TOJKatXE/b7QfXOv8R/gIXEOM3WxBjetcC7ZvaJ5GdDQtF0qkeUysUZZvZLM+vj7tOJ96Sy2ruytdA/gH6pl9YpLHYaPoMwnS1y9xeJiRJ3AG9ktmHpm9JUuBy2Ue7vIsrjBRl/+wNn2tL7ci0fdLU2q/dBmJQeYcnc/7uJnsEYwm76daIrXHiKZZI1hyq7uRIVxh/S/89S4krlStyJ1tBvSWMThCnhSsJ08Uc6uTK5Hbm7EIOMO6bzIcB30v8xhEnrq4Qt/L6y5LPEVFGxSffLXT+MUGZ9KMEWXS95wCjgDZbsvrsf0WPtT4xXfKOe+ZeLywrEFPkriKm5LcD3CcV5KjCDTi6IJBTuQ8TCzuuBuzPXxqZyuS8xKD2zs3JSeIOJXlVlCr5lrvUnFNj3CHP3DAosfsyEuyzlfiYFx+6a9ejyCJSeoHiZPp45P7lSwaXz04A/ZzK3tIwFjidtmVF5iVl60PQsYrfVmRTcnoHovo8GVkzn/YkZUreSekKEyesEYqyitJXRKdyWVNFdk3nu95IGOpPbGGI1+OyiFSGwUu58MmGL3o4lyrQvMcHhgaL5Sm63gzLlpednhDKZztI7LvyaJdOp1yYGaUvNvxT2NixtRluDqPCvJxoBH015/EOiAdbZLVI2Av4GfDXj9kuWXtC5E9GLeLSE92QIcEs711dPFfz7puYWkFlrub+/zPqm2Y4uj0BpCYnCuRrRTX4c2C25TwJ+kPN7FZl57SXIHkxMAT26UmhyL1MP0rRfolVUtKJbh2hZ3UkMAg9J/yel66cBt5BmplD+yuhVKuES5pCziVZeRX6lQFl6NkVXtm9AzGbaJOd+FDFwW1nwNpZYsV10S5aGySN6Ab9M78XJqTLvnbm+epnvaibcYYTyn0Jsi3In8CVCgRxNKJTR6d3t1Borose/JrGjwzEs2YtuOrGQ73rSzgfpPRlUID2jid7NwPROVnrnPVii/HcglOgqFBj7ychsaLlv9qPLI1B6guJbEjenF3Z3YlreQ8AXifnx2xItoKElyfsUYYJYg1jM9UilMiAtNEv/j6KEAX5ittAfUkH9ekrrX8hsIkjY1c9JhbXsDxbtTKyfGJvOW1NleGNWFjHgfjUF5+ynwjowVW6nk9vcj5g2ey+huHqSWbHdSXm9iKnaJ9VDHjHoexZhdh2e3EYRSuq5bB6WmW+ZcMeQpjYTU4FvA94E/jvjZyCh2E4m1yNcBjmVL4cOJGa3nUSMxVxJjCVU3qPLiRZ7p+QkWbulcjcqnd9GZvJJxt+hqX4ovGFro8t9dzi6PAKlJGLp1sBuqSLdizAZbEfsETWdsM3OJC1ILEHuboSNe4uM24WpEs3G6YuEHXdASfK2yrj9N2GGGJ6rzFuAtUt+zrsTA/0TcnFYnRicPjWd70/MYCvaQ9iYGDTtR7ReTyJm6GyU83f5slbqbcjbhdiM8SPAxwjzRWnyUv49mCrVq4gxiV7EeMUoYrziqKLpaEf+OMLkeFCm4htK9EpOzPn9EAV6RMT43RvEtN+NiN70KYR5Kbsf3QpF8o5YHPgmsH3GbR1iavpPSdvRpHey8FqjTD42rNx3l6PLI1BCxm5CtIB2SOeWCuUPiAG/6cRMGCNaEZUdQosuSBxL2LIfIu3pwxKzzkXEAPsxROvuCYrbgt8nL3PtSKJnsD0lbZNfRf7wlI7tc+57ELb9vqkw3ZwKUBmFtiVVRrek8AcTCuVMlkzB/AKhuIru/rsHYR7djyVTxrcgFEphecTCvMUsWRE9ipiWOjTjZ2tiAsX36pB/H03vzvu27SEUyu+o0povIG9rQmF+izCDbkI0Ok4mTGuFNk1NMsYRjcM/EY2O7NTjtYne3nUpbfdRzmB7Q8t9dzq6PAIlZO72qZDeT9h8DybGECYT3et9ifnx+5Qoc0x6iccTLZ7rybS2kp+DifGab1H8+xy1yPsaMQBfl++qE/bmi3Nu5wJ/J1rYQ1NlcS4FdzvOhF+xdZ9HzKZaNeXp0cSg7mmEybLoGNT6hALcvsq1zYmpv38jTG3LLC89u92IyQhXZNx/RwwGTyLMPi2pEi40xtRGHD5F2r6GGFvch2iEHceSHXJnUmAvrBRGZcftPsSCx/OIdTPnENOL1yHMh1NIk0c6KWswoSi2TedTCYWRnb3VK72Tgylh36tGl/vudiwXK+DTXPibiFW62xGzQwYQs7UeIFYN/9Xja2pFZW1OFMTfeHwvep0kd1fiexP3FpXRWXlm9hXgBnd/tsw4pLC3Jlpc+xBrDdYibNC/Jcwmz7j7GWbWw9O3wDspZyuiIprh7m+ZWX9iM8C3iAHNz7j7y2a2I9EifMrdny6QNCw+f3uCu09MX9NbTFRKlW+a9yFMXyt2Rp6ZnUhUat8hxgl6EUp4DFEhDiBavJVdEf5TJD0ZuZWvMHr6suchRH4dQnxH5k2ix/eiux9uZoOJdD/TCVlbEg26e4n35B7CEvBlwow2imgI/C+xIBN3X9CZNKX0GKG45meuXUk0DLZNfnp6STsFNLrcd0u6WpuVdRAtv9kssT8fR+pKU8LGbSmc3akyrZcYhP4qUVArawYs+1tveXV6piuxpHewNtF63z2d98ik8dvEjJ1CewxR2yy1W0lmqJLemb2JlutsMpvusWSX401JA7sF5VyR/vchxlxeZ+mB2n4UmM3Uhtz85oLfIQb/TyGtcSIWW95JsW+E7EDskHs28cnr85Os84jFgx8hFMnJ6T1ZoYCslsr7l3dL/68keuil7XXV6HLfXY8uj0CpiYmu/GyWbH/R6Ze2SthjCdtsZcrx6mS2zSBa6oengrl1d5NXRf6exNjT9cSitW0Ik+IrxPjCqsnfgUQrtNOfUE3h1DpLrbKP03vKrJPydiPs3qPT+UmEaXRwzt+3ibUWy9QgIcxWk4jxumGpguufrq1EKJSrynxHc/K3JQaJdyXzMSZys/uIXntlpXZnZZ1I+hYHMaX6LkJBnwrMA36c/G1MsUH9tYge1XsbQWau9cz8vwG4taTn2KXlsDsdXR6B0hMUmf/PIoWjSphrEKaPT6fz9VOBGZPztw4xblOohdloeVXk704o5R0J+/BE4hvkOxIK5W5i8Hgq5X1qt2Gz1JK8+cAXc2m+gDDRVFqZBxBjJMs8BkQMDl9EjPXcQph2jiU2GIVYf3ExsaFhPcrBF9I7dCYxi/EEohdS6WkOTu/OQyXlX6XntSoxFnNpOh9IwYZGTtaexESJ1TPvw/saFZQwg6qry2F3O7o8AnVJVPRQdqhDmA8QA7I3k7bpruKvlNlUjZaXDY/okYzLuX+SWBC6FdE6G04suFu3oLyGzlIjekCPEmsRDicz2yZViscTm1ReQnkzgIalZ/rbJPcaYlLITkWfXwdyf0WYljYhZtpdBnyXMAWPIcYvOrtFSkc9ryuS/NLNPYSifjKrUNLvjpRs9u2qctgdjy6PQF0TV/KLnCq+xcQg6XsvUHLfqUxZXSEvhd2LWIxVGRuxjNzDgGNLlDWGBs5SI1qQtxEmu7WImVTf4/228EGEPbzQzgEs3aP6JvCL9H9douc1tB55mJG5B0tMTHsQO0lPI3qBEyi2vqOre155hXI4sT9W6TvydkU57I5Hl0egux1EC/1xlmzXMJFowQ5bHuQlGd8jpjZWxkUqppH9gCtLkrE5MaV4ZDpfhyWDmdvm/H6laCWRKre1WXqrkhGpQjyakqYztyN/SL0q1hT+hsRakp0yFexGSRGfTJgj90zu+1Nij4iu63mNIxbRHkGYZbeoo6yGl8PudnR5BLrjkV7iR1Ildxd1XphUb3mZimhnYg3Cx1OFMLaiUJK/g4iVvoUGjWnwLLUk7z5iwPuk5FaZdTM8KZTJ1HHvJGK21mN5RVlS2BVTzHWEKWYeaTCYWGf1BjCxDnK7tOeVSftiGvD520aX++52dHkEuutBmA3ebtQLVS95uYroFuLbKBsSa0iuIRaX/RexXuBRMjODOimvK2bF/ZEwpW1BDA7ndyDelLDvH1FUUbYTDyMG90tdkJjSdw+ZQWHC3DQP2DKdX1CpbCl5r7aMzLr2vDqQvXIDZTW03Heno8sj0J2PRr7E9ZDXRkV0AmF7Xp8YYP8WYWf/OcVn/XTVrLjPpPNRxCSCs4Hzcn43po4mmSSjlPVOVdK3RzpfMXPtOGJMYQVij6rb65y2uvW8mu1odLnvLkeXR0BHF2V8+xXRicQ3st8bM6GkyQx0zay4B4mFczcnZTkoKdFSxn+6OB8/RZhe1kzn2TGhO1iyp1hdp63Wq+elo/scy/Vne0XbuPtLxJz9U8xsTY+tS3qna1OA54gppbj7YveoMUqQewMx4P0QsbDsJ2kLE8xsrJntlPyVsg1GkvddQqHc6u7HuftcYofgVjNbsww5XUVK31HAfWa2urv/x8wqn6F9hZjqTUpzPePhxPfV53foWSyXSJl8gOmgInoN+Hed5P6OWNMx0cz6ufu7ZjaR6BH9vY7yDjKzfsl5H2IR39tly2s07n4jseZjZsrHd8zsAGIb+X80MB6LGiVLNB9SJh9wOqiIXqij3JuJGUB3pQ0qDwYOcvfSlUlG3jeAP5rZ4cTMtEPd/fV6yGs0mXy8Mz3Pw4CD3b1ueShEluVi12BRHDMbB/yI2Pvqi0RF+2gD5O5BfGZ1S3eftbzJazTLe/pE8yJlIt6jqyoiM1vZ3d9cXuU1muU9faI5kTIRS6GKSAjRGaRMhBBCFEYD8EIIIQojZSKEEKIwUiZCCCEKI2UihBCiMFImQgghCiNlIoQQojD/H+s4LLMSeouzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2622ec18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a26c1df60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode: 0\n",
      "End of test PF value: 40598.0\n",
      "Min of test PF value: 9695.0\n",
      "Max of test PF value: 41810.0\n",
      "Mean of test PF value: 22236.0\n",
      "Max Draw Down of test PF value: 5005.0\n",
      "End of test weights: [0.03096487 0.09727165 0.09719112 0.0988317  0.09718989 0.09625425\n",
      " 0.09670539 0.09652934 0.09704168 0.09529121 0.0967289 ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a28a4dbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XzIam8z3M7Aoze8TMHjezbxYbfSGEEO2hTdE3s+7A+cAEYDiwv5kNzwU7DHjV3dcHzgHOSuf3BXq5++bAtsAXKxWCEEKI5lNLS38kMNvdn3b394CpwMRcmInAFenztcAuZmaAA73NrAVYEXgPeL2QmAshhGg3tYj+OsCczPHcdK5qGHdfAPwb6EdUAG8BLwDPAd9391fyBszsSDObaWYz58+f3+5ECCGEqI1aRN+qnPMaw4wEPgAGAMOAY81s3SUCul/s7iPcfUT//v1riJIQQoiOUIvozwUGZY4HAvNaC5NcOX2AV4ADgJvc/X13/yfwJ2BEvZEWQgjRMWoR/fuBDcxsmJn1BPYDpufCTAcOTp/3AW5zdydcOjtb0Bv4KPBEMVEXQgjRXtoU/eSjnwTcDDwOXOPus8zsNDPbOwW7FOhnZrOBY4DKtM7zgZWBR4nK46fu/teC0yCEEKJGWmoJ5O4zgBm5c5Mzn98hpmfmv/dmtfNCCCE6B63IFUKIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIEiHRF0KIElGT6JvZeDN70sxmm9kJVa73MrNp6fq9ZjY0c20LM7vbzGaZ2SNmtkJx0RdCCNEe2hR9M+sOnA9MAIYD+5vZ8Fyww4BX3X194BzgrPTdFuAq4Ch33xTYEXi/sNgLIYRoF7W09EcCs939aXd/D5gKTMyFmQhckT5fC+xiZgaMBf7q7n8BcPd/ufsHxURdCCFEe6lF9NcB5mSO56ZzVcO4+wLg30A/YEPAzexmM3vQzI6rZsDMjjSzmWY2c/78+e1NgxBCiBqpRfStyjmvMUwL8AngwPT/U2a2yxIB3S929xHuPqJ///41REkIIURHqEX05wKDMscDgXmthUl+/D7AK+n8He7+sru/DcwAtqk30kIIITpGLaJ/P7CBmQ0zs57AfsD0XJjpwMHp8z7Abe7uwM3AFma2UqoMxgCPFRN1IYQQ7aWlrQDuvsDMJhEC3h24zN1nmdlpwEx3nw5cClxpZrOJFv5+6buvmtkPiYrDgRnufkOD0iKEEKIN2hR9AHefQbhmsucmZz6/A+zbynevIqZtCiGE6GS0IlcIIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUqERF8IIUpETaJvZuPN7Ekzm21mJ1S53svMpqXr95rZ0Nz1wWb2ppl9o5hoCyGE6Ahtir6ZdQfOByYAw4H9zWx4LthhwKvuvj5wDnBW7vo5wI31R1cIIUQ91NLSHwnMdven3f09YCowMRdmInBF+nwtsIuZGYCZfRJ4GphVTJSFEEJ0lFpEfx1gTuZ4bjpXNYy7LwD+DfQzs97A8cCpSzNgZkea2Uwzmzl//vxa4y6EEKKd1CL6VuWc1xjmVOAcd39zaQbc/WJ3H+HuI/r3719DlIQQQnSElhrCzAUGZY4HAvNaCTPXzFqAPsArwChgHzP7HtAX+NDM3nH3H9UdcyGEEO2mFtG/H9jAzIYBzwP7AQfkwkwHDgbuBvYBbnN3B0ZXApjZKcCbEnwhhOg82hR9d19gZpOAm4HuwGXuPsvMTgNmuvt04FLgSjObTbTw92tkpIUQQnSMWlr6uPsMYEbu3OTM53eAfdu4xykdiJ8QQogC0YpcIYQoERJ9IYQoERJ9IYQoERJ9IYQoERJ9IYQoERJ9IYQoERJ9IYQoERJ9IYQoERJ9IYQoERJ9IYQoERJ9IYQoERJ9IYQoERJ9IYQoETXtsrksMfSEGxpy32e+u0dD7iuEEM1ELX0hhCgREn0hhCgREn0hhCgREn0hhCgREn0hhCgREn0hhCgREn0hhCgREn0hhCgREn0hhCgREn0hhCgREn0hhCgREn0hhCgREn0hhCgREn0hhCgREn0hhCgREn0hhCgREn0hhCgREn0hhCgREn0hhCgRNYm+mY03syfNbLaZnVDlei8zm5au32tmQ9P53czsATN7JP3fudjoCyGEaA9tir6ZdQfOByYAw4H9zWx4LthhwKvuvj5wDnBWOv8ysJe7bw4cDFxZVMSFEEK0n1pa+iOB2e7+tLu/B0wFJubCTASuSJ+vBXYxM3P3h9x9Xjo/C1jBzHoVEXEhhBDtpxbRXweYkzmem85VDePuC4B/A/1yYT4NPOTu7+YNmNmRZjbTzGbOnz+/1rgLIYRoJ7WIvlU55+0JY2abEi6fL1Yz4O4Xu/sIdx/Rv3//GqIkhBCiI9Qi+nOBQZnjgcC81sKYWQvQB3glHQ8EfgUc5O5P1RthIYQQHacW0b8f2MDMhplZT2A/YHouzHRioBZgH+A2d3cz6wvcAHzT3f9UVKSFEEJ0jDZFP/noJwE3A48D17j7LDM7zcz2TsEuBfqZ2WzgGKAyrXMSsD5wkpk9nP7WLDwVQgghaqKllkDuPgOYkTs3OfP5HWDfKt+bAkypM45CCCEKQityhRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiREj0hRCiRNQk+mY23syeNLPZZnZCleu9zGxaun6vmQ3NXPtmOv+kmY0rLupCCCHaS5uib2bdgfOBCcBwYH8zG54LdhjwqruvD5wDnJW+OxzYD9gUGA9ckO4nhBCiE6ilpT8SmO3uT7v7e8BUYGIuzETgivT5WmAXM7N0fqq7v+vu/wBmp/sJIYToBMzdlx7AbB9gvLsfno4/D4xy90mZMI+mMHPT8VPAKOAU4B53vyqdvxS40d2vzdk4EjgyHW4EPFl/0mpiDeDlJtmSveXDpuzJXle1OcTd+7cVqKWGG1mVc/maorUwtXwXd78YuLiGuBSKmc109xGyt2za6wybsid7y4LNpVGLe2cuMChzPBCY11oYM2sB+gCv1PhdIYQQTaIW0b8f2MDMhplZT2JgdnouzHTg4PR5H+A2D7/RdGC/NLtnGLABcF8xURdCCNFe2nTvuPsCM5sE3Ax0By5z91lmdhow092nA5cCV5rZbKKFv1/67iwzuwZ4DFgA/D93/6BBaekIzXYpyd6yb1P2ZG9ZsNkqbQ7kCiGEWH7QilwhhCgREn0hhCgREv2SY2YrdLL9Hk2219QV4Y221wXyr7eZNVxHmplvZtarWbY6A4l+F8LMNjezjc1s1SbZ2xU408w2boa9KvZ3Ar5kZps3yd5ewORmCUij7XWB/BtPbLnyyTRVu1F2dgQOMbMtGmUjY2sscFL63xTMbPtmVmoS/aVgZpua2RFNsrU78DNgO2DNJtk7H5gJrNJoe63YvwB4ntoWCdZrbyxwGvC7Zswga7S9LpB/ewHfA64H/uTuCxpkZw/iPXkN+LARNjK29gJ+ANwDPNFIWzm+DDzZjB4TAO6uv9wfsZK4G/A54HtNsLczsS/R9k1K33rAo8CYTnq+mxGF6mNNsjeOWAZ/XuZct2XVXhfIv8HAvcB26bgyC7DQZwpsRWzJMqoJaVof+AswInfemvRMfww80Mj3svKnln51Wtz9Q+DPwHgz+2QjjKRN6QB2Bc5y9z9XunmZa42gO/B3d7+jCbaqsTrwgLvf3ejWTWq9nUkUqnfN7CgAd/+wEbabZK+z8+894EXgETPr6Um1UpkhLeIsgrWIXsS9jXQfJYzYWHJmNv6VtBVuzKy/mWV7aMcQjaG7Gu3qkejnSP7RKWa2hbs/DZwIbGFmPRogEgPT/7VZ5OKoFCBP8dnczFYuwpiZjTazTwNvAKuY2ZCKrUxlM8HMNi3CXhX725nZtsRCvYXptUQKs3MRvlsz65YGiQ8AvubuJwIPAVuZ2RdhoRAXIpjNsNcF8m9XM/sssBrhguzl7u+ltFfyb11g7zrTOcLM1gd6AL1h4SLR7Hvy8YLekx3NbBdi65g1k618mjZN4xeFkNJ2OrFdPWb2EeAm4IvEQtYHGtkYkuizREupNzAfuNzMjiVa4ZsDfQoWid2BW1JhvQPYy8yGJBstGTs7A23unFeDvfHA/wJziFXTrwO7Vuz4Ir/zpsTWGYW2rJL9y4F3CFfWJ8zs0EwrsdKiGg5sV8BL38Pd33f3/d39znTuRuAPwNYZIfaC8rSh9rpA/o0F/geY4+6PE+6lX5rZKqmFX8mv3Ygddjtk3+KHln5B9GbuId6TI2DhO1J5dlsDm9ZZuYwlemSvu/tM4E0zuyLZyqZpNLBNES1wMxsDbAM8BexsZl8gtquZ6u6Xu/sRwCPAPxom/M3wV3XlPxb5I3cAvkK8tN2BTYA9geuIltU5QPeCbI4DZpF82sAw4GzgVGJ71Eq4/YkXf0AB9v5JxocOjCV8mIcDm6RzB6V4bVTwM67Y3zZzbifgJeDQXHofATYswN5VwG3Ab4gW1WrpWh/gs8Qg6FcLTF/D7HWR/Hud2Ealcm514MKU5rWAvimdDwGb1mHnBTJjWymddwFHZs59NqV9gzrT9BqwX+bcUOBq4OcZXdgXeLDyjOt8jmOBxyvlAPgS0eC7uErYi4F1i8zHhfduxE2XtT9gj/QSTQLuBo7LXOsFHAhcBKxUUMa/BFwDDMud/x7we+BY4CRiEGuzgtL2LDAld21XolV1F1G5PVSvvSr2xxOtwkeA7+au7QQ8A/wK+GUSrHrTW6lQPwWMAL5L9DC+RvTWIGa7HAT8EOjble11kfz7K1FpTQU+ns5bSteFwO0pD+8EtqjjOT6Z7vXNzPkVgF2IHs7/EaL8GLB5nXn2KFFR/wRYP51vIVyu1xM9tOuIDSI7lKYqNv9DppGTzh9OCPzewKpF5l2rcWmGka78B6wInAcMSCL0ILB2urZqJtyfgF3rtDUW+BtwCHAccAbw0cz1dYjN6n4AfAvYuE57g4EbgK3T8b3A/+TCrEy4j9YF1ij42Q5Pz237VKBuAi7IhVkb2AL4KPX3aEYDTwO75M4fAlwJ7JRL9ypd2V4XyL81kvhVhP7LSRA/lgs3kGj5r9ZBO1sSFdtOxFbsPyMmNmTDrEn0xncABtaRpkFJyD9BVCgnA9OA9XLhNgKGAP0LeI7jiIrzEuCP5CoR4P8Rlc++QO8i87BqfBptYFn4I1rY04nZOoPTuQmpUPdIL/Vs6uxuJWHbPn3enBD9KTRgShrQjxhwG5g51zcvHDRoihgxNtKLjKsmFbgbgfMbZPMY4NfELIheuWvnAdcuK/a6QP4NSPnXJ3OufxKo37K4q6nDcUg2WkiuGqAn0WO6glzPsIA09cv+T5+HApOJXkzh7hSiQfMz4BPp+BtEw3LTXLhjgR8BKzciPxez1WgDXe2PRb66dSq1O9GFvQc4OB1vT7TIP5H53up12NwROAo4IHd+U2IUfwq5+cF1pnE8sWjn3FRIuwE907U+STjOaeAz3p1o1Z9D6oaTxkOICvRGci3WOu1tTrSKjXCLXUAMgPfIhBlTlOg32l4XyL89k437gMNz1yrC/2tgdAHvybXEYGpWiLsD2ybhP72gNO1F+M/vBHbLXasI/89Jrp6CbO5GuIU3TscV7TmmFeHvUE+p3fFqhpGu9pdegEeIBULHpxf5GGD3y7j7AAAR6klEQVQG4Td8FNgzm1F12Kq4dL5K+PLzwr8ZsXLzHGCbAtK2OzEusTdwaDVxAFYl/KeFtqTSvccTvuXxwGXA/1YJsw7Rqzq7AHsTUj7ulo67EQPiF2aFmBikvyQJSofztNH2ulD+bUk0fh4E1sqF6U+0WKcSLpJ2P89k5y/EuMR1wKW56z2IWS7XAScXkKYHiYrk0PR8e+fCDCXGYy4l1unU+xzHkllAx5I9wWOIinXLovOwzbg122Bn/wEbE37JjYhZMzNTAV2R6NJ+lFzNXIetcenF/mg6PpwYsd85F25LYj1AXf5DYqXm68BX0vE2xDYHU4jpftmW6KrA0IKf7ebAc6TWIdG1vRv4NrG6uW8m7ACSK60Oe2OI2RB5n7oRlfnFRKV6KFHJD+/K9gi/fGfm39AU72PTcV+iwTKVGGManQlbjw+/MmXxgHS8GdEz/DZRca6ezrcQq3I7PNaTyvnfgKPS8VqpTF6ayuPmmbCDKGBcpEq5H0T0JPrnwp1IDFz3pEkrf929XD+ikhZBnEy8SBPd/Z9mthHRjfydu08u2N6ZwGfcfT0zW50oUDcQ7p7rgRPc/b0UtmflcwdttXgsYJlMiMd1RGvsNuLFOhRYiZjuZl7wfjBpFeNgYoD6bkL8zyLS+x/CrTMH+D7woRfw4pnZV9O9/tfM+hHuslHAi+5+pZl9nXBVrA3s4+6PdWV7ZrYi8HVgQ2I2zLE0L//6EFOTTwfeJwYev070fO8CPkNUbpPd/e06bY0m3s2LgX8R7rEbSGMIxGDnVfW+Ixa7ZfYketGziAp7MjGt9m9EL2MesdXK+/XYytk9E9jX3dc3s/6EO/NSd7+wStjV3f2VomzXRLNql876I1eDEjMEphGt+4+kcxsTXdpC/HlEC3ez9PnHxHjBvSxqAa9JTDs7oiB7GxIFsx9RMP+bKEw/zITZmHC39CrCZs7+eFL3PD3fc4iW3OmZMIcCFxZkrzIVcn/C1TKOqESvIX7WcwZpzICYhrteV7ZHVBxHEu6HDYCjiQVY52TCNDL/1iXGDz5OtK5PTeXh3Jz928m5etpppyXzeXdiuunjwImZ88cBlxSQpnHEYrJtiFlIPyY3bTi9q7+joBkzVcr93ancH5ELV1cPt96/5XpFrpmZu7uZjTWzk8zsdMKd8xOicH3azNZ29yeIaWmzC7A5gXiZP2Zm67n7UcTg0eru/hMAd/9nClPUXvJPEy2za4gu99nEYq/BadsDiAK9MdGaKoy0gvIMYv407v4HYvDqZmB+6klVGGKx/3o9qyiHAtNSa/FXhBCeRcyuOoPwuf830SrG3X/k7k91VXvp+f2UcCueR0wlvIbwLw9qdP4l3gXeJMYRtiXGmK4DPjCzT6QwGxAVQod6GKlcnGtmPzez3u4+g3hPKqtPK1uSvAD0Tb2eDmGxM+e5hMtogbu/TAy43wG8ldm+oU9KU93lsJVyfxdRHi/JhDsQOM8W33enuXRmjdOMP8KV8giL5k7fTbS0xxB+va8SXcC6p74lW7OpsvshUbD/kD5/mgJXTlbiTrQufkvynRNd6KuJLvsf6eBKyaXY3ZUYrNopHQ8Bjk+fxxCunC8Tvtr7irLPoi56xWfaN3f9KKLS6U0BvtJG2QNGAm+xaLfKA4ge4ADCn/61RuZfLi49iKnLvyCmTLYA3yEquLOA++ngwi+iYnyYWMB2PXB35tr4VC73JwY3Z3bUTrrfYKKXUpkabZlrA4iK5tuEm/d+6ljklblve8r9TOocW6o7vp1pvCEJikz/eOb49IoQpeOzgT9nMqGwDABOIS21r7xsLD749iNid8KZ1Lmsm+i2jgZWSMcDiBkxvyf1LAhXz6mEL72wlZrpvi1JkK7NPPd7SQNm6dwYYnXq4/UKFrBi7vgEwle6PYsqvT7EQPmD9eYrudXXRdpLz88I0Z/B4ivAf82iaa5rEoN9heZfuvd2LO4+Wp0Q5uuJyvqjKY+/SzSUOrq1wobA34EvZ879nMUXru1MtMofLeA9GQLcupTrqyUhXmLKZB02ay33DxSpNx2Ob2dHoLCERCFalegePgGMS+cnAWfkwk4jMy+4ANuDial536q83LlM70aajkm0MuoVpLWIlsqdxGDikPR5Urp+NnAraSYCxa/UXLlyX8INcD7RaqrYr7z4lp5NvStt1ydmr2ycO38cMQBYWdgznlhBWu9WDk2zR7Sqf57ei9OT6PbKXF+tyHc1c99hRCU9mdhO4U7gCELov0UI/+j07nZojQrRg+5HrDA/kUV7Tc0gFixdT1qJnd6TQXWkZzTRWxiY3slKb7cbiyrpHYnKbmXqGJvI2GxquS8s7zs7AoUnKPYyvyW9WLsT06UeBj5PzC8eRbQohhZkbw+i6706sWjlkUqhJS2oSZ+Po4CBYmJ2yB9SgfpqSutfyGzmRfh9L0yFqugfttiFmH8+Ph33T6J1Y9YWMXB7DXXOeU6FamASoXPIbbJFTGe8l6hgupNZQdpBez2JKbRTGmGPGDz8EeFuHJ7OjSQqk+ezeVhkvmXuO4Y05ZSYonkb8DbwhUyYgUQFdDq5HlY77FR+CW4gMZtpCjFWcDXh6668R1cRLeAO2Um2xqVyNzId30ZmEkMm3JFJH+reOLHZ5b7Qd6CzI1BIIhavXcclwdub6CpvT+wBM4PwHc4kLbwqwO44wge7VebcpUnssnH6POFnXKcge9tkzn2B6H4Pz4luC7Bmwc95d2LAeL9cHFYjBjnPSscHEjOW6m1xb0QMvvUlWoNTiBkZG+bCXdVe8W3F3q7EpmhbAh8juu2F2Uv591ASv2mEz7wn4U8fSfjTj6s3HUuxP4FwtR2aEaihRCv/tFzYj1BHD4MYX3qLmI65IdE7PZNwq2T3m+pRT94Ri6DeBnbInFuLmDL8Q9I2FumdrHutRiYfm1buC38POjsCBWTAxkSLYsd0bKnwnEEMHM0gZj4YUStXdtSrd+HVeMLX+jBpzw4WuTMuIwZqTyRaS09Sv69yCXuZa8cSLe0dKGj75yr2h6d07JA7vyfhe+6TXvpb0oteROFqSaJxa7r/YEL4z2PR1LjPERVMvbtl7km4BQ9g0VTerQjhr9sesQDpQxat0BxJTBccmgmzLTEQ/+0G5N9H07uzxHYfhPDfRJXWcR32tiUqtm8Q7r+NicbB6YRLqa7NC5ONCUQj7k9E4yA7JXRNovf0q5S2+yhm0Lap5b4Rf50egQIyYYdUmB4gfJKHET7uE4hu5f7E/OJ9C7Q5Jr1sE4kWxPVkWi8pzGHEeMI3qH9/+FrsfYUYyG3I784S/tDLc+d+DPyDaLEOTYX6x9S5O2jm/hVf7EXE7JlVUp5+ixgcPJtw1dU7RrIeUVHtUOXaFsSUzL8TLqZ220vPbhwxqP2LzPmbiEHFSYS7oyWJZV1jIK3EYQ/SthfE2Ne+RGPpZBbtKDmTOva6Sfeo7FDbm1jYdRGx7uBCYtrnWoTbbDJpEkIHbQ0mBH1UOp5KCHt2tk7P9E4OpoB9bZpd7hv1t1ysyE1ziW8mVg1uT8wGWIeYnfMgsYrxbx6/jlOvrS2IAvMbj9/TXCvZHUvsd35vvTY6as/MvgTc4O7PFRmHdO9tiRbMvsRc7TUIH+lvCXfBs+5+rpl18/RbqR20sw0hGPe7+ztmNoDYlOsdYmDsU+7+qpntRLSwnnb3Z+pIGhY/O3iqux+Sfh3pQ0I8Kr/52ptw+azQEXtmdhohPscTfuyeRGU5hhCudYgWZGWV9rv1pCdjt/KrWp5+qe1wIr8OJ37H4G2iB/Wyux9tZoOJdD/bAVtbEw2ve4n35B6iZ/1Fwn00kqiw/4dYeIa7z+9ImlJ6jKhg5mWuXU1U4KNSmO5e0MrlZpf7htLZtU5Rf0RL6nEW+UdPJnUhKWADpXSf3aky3ZIYzPwyUaAqc64t+7/R9hr0TFdkUWt7TaI1vHs67pZJ438TMzTq2kOE2mYl/Z7kfinondmHaAk+TmbzKxbtCroJaYCwTju/SJ97E2MCb7D4gF9f6pi90ord/CZfxxODyGeS1ogQi8rupL496nckdpQ8n/ip0YuTrYuIRVJbEoJ/enpPetRhq6Xy/uXPpc9XEz3ewvayaXa5b/Rfp0eg0MREF/ZxFi2b7/DLVeXe4wnfYWUq6GpkltsTLd+jUwHadlmzV8X+XsTYyPXE4pztCFfaa4T/e5UU7mCiVdfhn65L96l1VlJln5aFlU4H7Y0j/LKj0/EUwiU4OBfuv4m56u1qOBDumknEeNKwJEQD0rUVCeGfVuQ7mrM/ihhsHEvmRzvIzeYiesGVlaMdtXUaaS94YqrrXURFehYwF/h+CrcR9Q0Or0H0UBZuyJa51j3z+Qbg9wU9x04thw15Nzo7AoUnKDLppXpe4ir3XJ3o8n8yHa+XXuwxuXBrEeMKdbXYmm2viv3dicpzJ8J/eQjxG607EcJ/NzEIOZXifuKwabOSkr15wOdzab6EcE1UWm0HET78do9REIOMlxFjEbcSLo2TiI3+IOavX05sLNaIcvC59A6dR8xaO5Vo1Vd6boPTu/NwQflX6cmsQowV/CwdD6TOBkHO1l7EgHvlN4hbqFL5U8CMmc4uh4366/QINCRR0eLfsQH3fJAY2LuFtP1slXBF/Xh6U+1l70e08Cfkzu9GLHzbhmjtDCcWFq1dp72mzkoiehSPEnO5jyYzuyKJ1ynEZnFXUNyMj2Hpmf422b2WmFywc73Prw27vyRcKhsTM6uuBL5JuEDHEP71jm6t0FZP5hfJfuFuDqJCfSor/On/ThTs7uysctjIv06PQEMTV/ALlwTqQ2KwbWFGp/M7F2mrM+yle/ckFp1UfPeWsXsUcFKBtsbQxFlJRIvsNsJVtQYxc+bbLOmrHUT4a+taycziPZSvAz9Nn9cmejJDG5GHGZt7ssi1siex8+p0ole1H/XNj+/snkxe+I8m9r8pfAfLziiHDX0vOjsCy9of0eJ9gkXLvA8hWoTDlgd7yca3iSlnFb99xSVwAHB1QTa2IKZ6jkjHa7FoUGxULuyX6i3MSYTWZPEtDjZNwlX3j9DXYH9IowQw3X8DYi7+zhkh3DBVmKcTbri90vkDKbCHQef1ZCYQiwWPIdyRWzXQVtPLYcPS0tkRWBb/0sv2SBKju2jwAoxG28sIxi7EHO6Pp4I7viL8KdyhxMrDugYfafKspGTvPmLgdEo6V5llMTwJ/wk0cG8UYnbOY/kKraB7V1wQvyJcEHNJg4rEOpW3gEMaYLdTezKZtH9IE352sNnlvmHp6OwILKt/RHf5vWZlfKPs5QTjVmJv/g2IOfjXEoto/ouYb/0omZkgHbTXGbOg/ki4kLYiBhnzO3ZuQvifj6m3QltKPIwYJC504VVK3z1kBhcJN8tcYOt0fElFFCl4L6aMzYb2ZNqwvVITbTW13DckDZ0dgWX5r5kvWyPstSIYpxK+0fWIgdpvEH7gn1D/LI/OmgX1qXQ8khiMPh+4KBd2Ixroikg2ClkvUiV9e6bjFTLXTiZ83j2IPWhub3DaGtaT6Wp/zS73hce/syOgv07K+KULxmnEb4gu9OlT0KA4nTML6iFigdAtqVIblCq7QsYnOjkf9yBcDv3ScXbM4g4W7RnU0OmEjerJ6K/4v+X65xJF63j8GPNewJlm1s9jy4Ne6dpk4Hliqh/uXsgPmad73UAMnD5MLKD5Qdr6ADMbb2Y7p3CFLJ9P9r5JCP/v3f1kd59D7KjZP/3A+TJLSt9xwH1mtpq7v2tmlZ//e42YgktKcyPj4cTvz85rM7DoVCT6JaYNwXgd+E+D7N5EzIk/xMz6uvsHZnYI0cP4RwPtHWpmfdPpfYnFSu8Vba/ZuPuNxJz5mSkf3zezg4jtkV9oYjwWNMuW6DgS/ZLThmD8s4F2byFmfNyVNoo7DDjU3QsX/Yy9rwF/NLOjiZlIR7r7G42w12wy+Xhnep5HAYe5e8PyUCybLBe7bIr6MbMJxA9jX0D8+MOR7v5oE+zuSfy83dbuPmt5s9dslvf0ifqR6IuFdJZgmNlK7v728mqv2Szv6RP1IdEXiyHBEGL5RqIvhBAlQgO5QghRIiT6QghRIiT6QghRIiT6QghRIiT6QghRIiT6QghRIv4/z8pgRPfVnd4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a28aa4f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6+lB2cXExNjU1Xc7NzXXNzc3lFxUVtaekpHTzeDxTdnb2jYlj27Rp083U1FT//fv365csWaLduHHjbYFAMJSTk9ORn5/PLy8vbwUA2LlzJx8AoLm5uUmhUHCXLVsW2NbWdmnfvn0u7e3tVo2NjU0WFhbjctlotVpWQkKCf1JS0u2pMv5KpVLSnHhv1apVt3bu3HlzsnoajYY1MDDADg4OHpzqPqekpPhbWloOX7t2jfvrX//6GoczdRjw7bffcmma1k9XZzZhQIIQQuhHISwsTFdeXm577tw5XkZGxo1r165ZVlZW2jo4OJgkEokOAKCkpMT+7Nmz9jRN0wAAer2epVQquWMDkoqKCl58fLyGzWaDj4+PMTIysn9sP0lJSRoAAIlEoj9+/LgTzCAhIUEbFRXV8OWXXzqUlJQ4hIWF0Q0NDY0T61VVVfFef/31mwAAoaGhdzw8PO42NDRwy8rK7OVyebc5ey6fzx/Nq7N8+XLhli1bujZu3Djlss9USzYT/ZBVeNo65iWb69evcxYuXEj953/+p5YkybvTXvQvggEJQgih8aaZyXiUFi5cqKuqquIplUrriIgIg7+//909e/bweTyeKS0t7RbAyEN3y5YtnRkZGVPmWpkpR5s5QR2Hw2GMRuN97QPg8/kmuVzeI5fLe2QymfDUqVM8FxeXcQn7pur3h0Bh0sKIiAhdSUmJw2uvvdZjngV5WM7OzsPW1tbDTU1NljRNTxtkeHh4GMVisf7s2bO2UwUkzzzzzJ3Lly/bmEwmMC/ZPEqP3R4ShBBCTyapVKo7ffq0o6Ojo4nD4QCfzzdptVq2QqHgyWSyAQCApUuXag8dOuTS19fHAgC4cuWKRUdHx7gf19HR0bpjx445mUwmUKvVnPPnz9vN1LednZ2pv79/0qfu8ePH7fr7+1kAI8si7e3tVn5+fncdHBxMYzfeRkVF6Q4fPuwMAFBfX2/V2dlpOX/+/DuxsbHagoIC16GhIQCAcUs2u3fvvu7s7GxMTk72eeAbNoktW7Z0yuVy356eHhYAQE9PD8v8Ns1Y/f39rMbGRhuRSDTl8k5wcPDg/PnzB958802P4eFhAABoaGiwOnz4sONU1/wzMCBBCCH0oyCRSAy9vb2c8PDw0eUXiqIMPB7PZF6yiI+P17788ss9ERERFEmS9EsvvRTQ29s7LpBYu3atxt3d/S5JksFpaWm+ISEhA46OjqaJ/Y2VkJDQ+9VXXzlOtqm1urra5plnngkiSZKWSCRBycnJt6RSqV4ikRg4HA4jEonorKwst61bt940mUwESZJ0YmJiwIEDB65aW1sz6enp3V5eXncpigoWiUT0wYMHx70qfPDgQfXg4CBLLpd7TTY2qVRKml/FfemllwTT/T22bt3a/dxzz2mfffZZOjAwMHjx4sWUjY3NsLk8JSXFn6IoOiQkJOiVV165FR0drZ+uvcOHD1+9ceOGha+vr5gkSXrdunWCsRt8ZxMx09TWoxIeHs7U1NQ88HUfn/0Ocr6+DJeyfgI8K1xxQgj9eyEIopZhmPDZbreuru5qSEjII0k5Pxf6+vpYDg4Ow11dXeyIiIigyspKJWb8nXt1dXUuISEhgsnKHtsn+lwFUgghhH784uLiArVaLXtoaIjIyMjoxGDkx++xC0hm2ECMEEIIwYULF1RzPQb0YHAPCUIIIYTmHAYkCCGEEJpzGJAghBBCaM5hQIIQQgihOYcBCUIIoTm3bt067+zs7NGkc1FRUYGJiYm+5s/r16/32rVrF3+6NkJDQ6mZ+vH09Hy6s7Pznhc6iouL7UpLS20nu0atVnNkMplQJBLRAQEBwVKpVAgwkm23oKDAebJr7sdUY5lYhyRJ2nwOSWpqqndycrIPRVF0QEBAMJfLfdZc9umnnzolJCQIxiYSBPhH0kGVSmVpri8SiejQ0FCqrq7O6q9//au9uQ0bG5tQgUAgHnvmSXl5uU14eLhIIBCI/fz8ghMTE33NB8XNpsfuLRuEEEJPnsWLF+u++OILJwC4aTKZQKPRcMaeglpdXc1btWrVtEfaKxQK5cP2X1ZWZsfj8UxxcXEDE8u2bdvmGRMTo83MzLwJAHD+/HlrAICWlharoqIiZ7lcPmUemtkwVS4blUpl+eKLLwYqlcom83fFxcUO07Xl7e09aK6/e/dul6ysLPejR49eTUhIaAIAkEgkot/85jfq5557Tg8wEoytXr06oLCw8LvY2NiB4eFh+Oyzz5x6e3tZdnZ2w9P19aBwhgQhhNCci4mJ0dXW1vIAAGpra61FIpHB1tbW1N3dzTYYDERbWxt30aJFegCAzMxMvlgsDiJJkk5PT/cwt2GeCTCZTLBmzRofoVAYLJPJhFKpVDh21iAvL8+NpukgkiRphULBValUloWFha4FBQX8yU5q7erqshh7OumCBQsMAADbt2/3rKmp4VEURWdlZbnp9XpixYoVApIk6aCgIPrEiRN2AABGoxE2bNjgRZIkTZIknZOT4za2fZ1OR0RHRwfm5+ffc8T7o6TVatkznWCbn5/vtnLlytuxsbEDAAAsFgvS0tI03t7es36uy2M7Q4LHoiGE0KORWZnp3apptZnNNoVOQv2vF/96yhkOgUAwxOFwmJaWFsszZ87YRkZGDnR0dFiUlZXxnJycjCKRyMDlcpmjR4/at7a2cuvr6y8zDAOxsbHCkydP8sZm+y0sLHRSq9WWKpWqsaOjgyMWi8Wpqam3zeUuLi7Gpqamy7m5ua65ubn8oqKi9pSUlG4ej2fKzs6+MXFsmzZtupmamuq/f/9+/ZIlS7QbN268LRAIhnJycjry8/P55eXlrQAAO3fu5AMANDc3NykUCu6yZcsC29raLu3bt8+lvb3dqrGxscnCwmJcLhutVstKSEjwT0pKur158+bbE/sGGDk63px4b9WqVbd27tx5c7p7/c4773i9//777pOVqdVqK4qi6IGBAdadO3dYVVVV084qNTU1WaekpEw6rtn22AYkCCGEnixhYWG68vJy23PnzvEyMjJuXLt2zbKystLWwcHBJJFIdAAAJSUl9mfPnrWnaZoGANDr9SylUskdG5BUVFTw4uPjNWw2G3x8fIyRkZH9Y/tJSkrSAABIJBL98ePHx+23mExCQoI2Kiqq4csvv3QoKSlxCAsLoxsaGhon1quqquK9/vrrNwEAQkND73h4eNxtaGjglpWV2cvl8m4LCwsAGMkcbL5m+fLlwi1btnRt3LhxymWfqZZspvLuu+9+n5aWpjF/Ns8cAYxfsvn444+dXn31Vd+KioqW+237UcKABCGE0DjTzWQ8SgsXLtRVVVXxlEqldUREhMHf3//unj17+Dwez5SWlnYLYCRtyJYtWzozMjKmzLszU2oRLpfLAABwOBzGaDTe1/nffD7fJJfLe+RyeY9MJhOeOnWK5+LiMm65Y6p+GYYBgiAmLYyIiNCVlJQ4vPbaaz3mWZB/lVWrVvW+8cYbgunqBAUFGWpqamzWrFnT+6jHc19/e4IgXiAIQkUQRCtBEP/PJOU+BEGUEwShIAiiniCIZbM/VIQQQk8yqVSqO336tKOjo6OJw+EAn883abVatkKh4MlksgEAgKVLl2oPHTrk0tfXxwIAuHLlikVHR8e4H9fR0dG6Y8eOOZlMJlCr1Zzz58/bzdS3nZ2dqb+/nz1Z2fHjx+3Mb5VoNBpWe3u7lZ+f310HBwfT2I23UVFRusOHDzsDANTX11t1dnZazp8//05sbKy2oKDAdWhoCABg3JLN7t27rzs7OxuTk5N9HviG/ZNKS0vtvL29B6er89Zbb908cuTIvLKystE3kD788EPna9euzfqExowBCUEQbAD4AACWAgANAKsIgqAnVHsHAI4wDBMKAK8AwIezPVCEEEJPNolEYujt7eWEh4ePLr9QFGXg8Xgm85JFfHy89uWXX+6JiIigSJKkX3rppYDe3t5xgcTatWs17u7ud0mSDE5LS/MNCQkZmGnzZkJCQu9XX33lONmm1urqaptnnnkmiCRJWiKRBCUnJ9+SSqV6iURi4HA4jEgkorOysty2bt1602QyESRJ0omJiQEHDhy4am1tzaSnp3d7eXndpSgqWCQS0QcPHhz3qvDBgwfVg4ODLLlc7jXZ2KRSKWl+Ldf8Ku7DMu8hEYlEdGZmpmdBQUH7dPW9vb2NhYWF32VkZHgJBAKxv79/8N///nc7JyenWX3DBgCAmGlqiyCIhQCwi2GYn/zw+b8BABiG+Z8xdQ4AwHcMw7z/Q/18hmEWTddueHg4U1NT88AD/kPFd/DuV5ehftfzYM+1eODrEULocUYQRC3DMOGz3W5dXd3VkJCQKZdBHjd9fX0sBweH4a6uLnZERERQZWWlEjP+zr26ujqXkJAQwWRl9zPl4gkAY9cTvweABRPq7AKAUwRBvA4AtgAQ++DDRAghhGZHXFxcoFarZQ8NDREZGRmdGIz8+N1PQDLZhp+J0yqrAOCPDMPk/zBDcoggCDHDMOOmdAiC2AAAGwAAfHz+5ctlCCGE/k1cuHBBNddjQA/mfja1fg8A3mM+ewHA9Ql11gHAEQAAhmHOAQAXAO454IVhmI8YhglnGCbc1dX14UY82tY/dTlCCCGEfkTuJyCpBoBAgiD8CIKwhJFNq8cn1LkGAP8BAEAQRBCMBCTdszlQM4K4rze0EEIIIfQYmTEgYRjGCACbAeBvAHAZRt6maSQIIpsgiOU/VPsVAKwnCKIOAP4MAKnMTLtlEUIIIYR+cF/vETMM8zUAfD3hux1j/twEAItnd2gIIYQQ+neByfUQQgjNuXXr1nlnZ2ePJp2LiooKTExM9DV/Xr9+vdeuXbv407URGhpKzdSPp6fn052dnff8GC8uLrYrLS21newatVrNkclkQpFIRAcEBARLpVIhwEi23YKCAufJrrkfU41lYh1zUr6AgIDgN954w8NgMIzuXaipqeFGRkaSAoFA7OvrK87IyHAfHv7H+yRffPGF/dNPPx3k5+cXTFEU/dOf/tS/paXFEgAgISFB4Onp+bT5jBPz/du7d+88JyenEIqiaKFQGPzCCy/49/f3s7Zt2/aUuS6bzQ4z//ndd991AwD4/e9/Py8wMDBYKBQGBwQEBO/YsWPa/18TYUCCEEJozi1evFj3zTff8ABGsvVqNBqOSqWyNpdXV1fznnvuOd3ULQAoFIppE8VNp6yszK6iooI3Wdm2bds8Y2JitCqVqqmtra0xLy+vAwCgpaXFqqio6KEDkvt15syZ5ubm5qaLFy9evnLlitXq1at9AUayBL/00kvCrVu3dl29evXSpUuXms6fP897//33XQEAqqurub/61a98PvvssytXrlxpVCqVTUlJSbdbW1stzW2/++673yuVyialUtk09v797Gc/0yiVyqbW1tZGCwsL5pNPPnF6//33u8x1rayshs1/fuedd24eOXLE/sMPP3QrLS1tbm1tbWxoaGhycHCY9jC6iTAgQQghNOdiYmJ0tbW1PACA2tpaa5FIZLC1tTV1d3ezDQYD0dbWxl20aJEeACAzM5MvFouDSJKk09PTPcxtmJPImUwmWLNmjY9QKAyWyWRCqVQq/PTTT0eT6OXl5bnRNB1EkiStUCi4KpXKsrCw0LWgoIA/2UmtXV1dFt7e3nfNnxcsWGAAANi+fbtnTU0Nj6IoOisry02v1xMrVqwQkCRJBwUF0SdOnLADADAajbBhwwYv80xHTk6O29j2dTodER0dHZifn3/P26ljOTg4DH/22WftpaWljjdu3GB//PHH88LDw3Xx8fFaAAA7O7vh/fv3X/vd737nDgCQk5Pj/uabb3Y+++yzd8xtrF69um9sIsKZDA0NgV6vZzk7O08bXOTl5bnn5uZ+LxAIhgAAbGxsmF/96lcPdNAeJtdDCCE0zvW3t3sPtrTYzGabVoGBeo/3cqZM2icQCIY4HA7T0tJieebMGdvIyMiBjo4Oi7KyMp6Tk5NRJBIZuFwuc/ToUfvW1lZufX39ZYZhIDY2Vnjy5Ene2IdsYWGhk1qttlSpVI0dHR0csVgsTk1NvW0ud3FxMTY1NV3Ozc11zc3N5RcVFbWnpKR083g8U3Z29o2JY9u0adPN1NRU//379+uXLFmi3bj6rv8YAAAgAElEQVRx422BQDCUk5PTkZ+fzy8vL28FANi5cycfAKC5ublJoVBwly1bFtjW1nZp3759Lu3t7VaNjY1NFhYW43LZaLVaVkJCgn9SUtLtzZs3357Y90TOzs7Dnp6edxsbG7mNjY3cZ599Vj+2PDg4eFCv17N6enpYzc3N3G3btnVN194777zj9f7777sDAJAkaTh+/PgVAIATJ044URTF6+7uthAIBHdWrVo1bXK9lpYW68WLF+unqzOTx3eGBN/hQQihJ0pYWJiuvLzc9ty5c7zo6GjdokWLBiorK20rKip4EolEBwBQUlJif/bsWXuapung4GC6ra2Nq1QquWPbqaio4MXHx2vYbDb4+PgYIyMj+8eWJyUlaQAAJBKJXq1WW800roSEBG1ra2tDWlraLZVKZR0WFkZfv379nh/0VVVVvJSUlNsAAKGhoXc8PDzuNjQ0cMvKyuzlcnm3hcVIuhM+nz8627B8+XJhcnLyrfsJRszML7EyDENMdRTGxO+7urrYFEXRAoFAPHZvx9glG3MwAvCPJZvu7u66oKAgw44dO5663/E9rMduhgRPIUEIoUdrupmMR2nhwoW6qqoqnlKptI6IiDD4+/vf3bNnD5/H45nS0tJuAYw8jLds2dKZkZEx5XLATKdOcLlcBgCAw+EwRqPxvh4rfD7fJJfLe+RyeY9MJhOeOnWK5+LiMm4ZY6p+GYYBgiAmLYyIiNCVlJQ4vPbaaz0s1sxzBBqNhnX9+nXLp59++k5wcLBh4r6XpqYmSxsbm2EnJ6dhkiTvXLhwwWbhwoWGp556yqRUKpt27NjBH5uheCYsFguWL1/e+8EHH7hNV08oFBoqKyttli9f3j9dvWn7etgLEUIIodkklUp1p0+fdnR0dDRxOBzg8/kmrVbLVigUPJlMNgAAsHTpUu2hQ4dc+vr6WAAAV65csejo6Bj34zo6Olp37NgxJ5PJBGq1mnP+/Hm7mfq2s7Mz9ff3T/qgPn78uF1/fz8LYCQgaG9vt/Lz87vr4OBgGvtwj4qK0h0+fNgZAKC+vt6qs7PTcv78+XdiY2O1BQUFrkNDQwAA45Zsdu/efd3Z2dmYnJw8Yz6Vvr4+Vlpamm9cXFyvq6uracOGDberq6vtjh07Zgcwshdl06ZNPq+//noXAMDbb7/dlZ+f737x4sXRGSS9Xv/Az/2Kigo7gUAwOF2drVu3dr399tte165d4wAAGAwGwvz2zf167GZIEEIIPZkkEomht7eXEx8fP7p8QVGUYWBggO3u7m4EAIiPj9c2NjZyIyIiKAAAGxub4c8///yKp6fnaPK8tWvXak6fPm1HkmSwn5/fnZCQkAFHR8dpN2UmJCT0rlixIuDkyZOOe/bsufbCCy+M7kmprq62SU9P92Gz2QzDMERycvItqVSqHxwcJDgcDiMSieikpKRbW7duvZmcnOxLkiTNZrPhwIEDV62trZn09PTu5uZmK4qigjkcDrN27drut99+e/Q084MHD6pXrlwpkMvlXgUFBd9PHJtUKiUZhiGGh4dh2bJlve+///51AAAej8ccPXq0dfPmzT5btmyxGB4ehpdffvn2f//3f98038+8vDx1SkqK38DAAMvJycnk6ek5mJOTM5r+ZeweEgCAb7/99jLAP/aQDA8Pg7u7+90//elPV6e7f4mJiX1dXV2c//iP/xD9MCMEq1evfqBNrcRcHagaHh7O1NTUPPB1n/z9CmQXN0HdjufBwcbiEYwMIYR+vAiCqGUYJny2262rq7saEhLyQA+QH7O+vj6Wg4PDcFdXFzsiIiKosrJSiRl/515dXZ1LSEiIYLIynCFBCCH0xImLiwvUarXsoaEhIiMjoxODkR8/DEgQQgg9cS5cuKCa6zGgB4ObWhFCCCE05zAgQQghhNCce2wDEgZPRkMIIYSeGI9dQDLFoXQIIYQQeow9dgEJQgihJ8+6deu8s7OzRw/SioqKCkxMTPQ1f16/fr3Xrl27pk1nHxoaSs3Uj6en59OdnZ33vNBRXFxsV1paajvZNWq1miOTyYQikYgOCAgIlkqlQgAAlUplWVBQ8NDZfqcay8Q6JEnSFEXRFEXRqamp3snJyT4URdEBAQHBXC73WXPZp59+6pSQkCAYm0gQ4B9JB4uLi+1kMplwbNnY+n/+858dgoKCaPPfc/fu3S4AAG+++aaHm5vbfIqiaF9fX/Hzzz8fUFtbywUAiIuLC6Aoivbx8RHb2dk9Yx7LVPdyOviWDUIIoTm3ePFi3RdffOEEADdNJhNoNBrO2FNQq6ureatWrZr2SHuFQqF82P7LysrseDyeKS4ubmBi2bZt2zxjYmK0mZmZNwEAzp8/bw0A0NLSYlVUVOQsl8t7Hrbf+3HmzJlm88FwY6lUKssXX3wxUKlUNpm/Ky4udniYPgYHB4lf/vKXvufOnbscEBAwZDAYiObmZktzuVwuv2FOPPjxxx87/eQnPxHV19c3lpaWtv3Qr93YRIMPA2dIEEIIzbmYmBhdbW0tDwCgtrbWWiQSGWxtbU3d3d1sg8FAtLW1cRctWqQHAMjMzOSLxeIgkiTp9PR0D3Mb5pkAk8kEa9as8REKhcEymUwolUqFY2cN8vLy3GiaDiJJklYoFFyVSmVZWFjoWlBQwKcoii4pKRmXH6arq8vC29v7rvnzggULDAAA27dv96ypqeFRFEVnZWW56fV6YsWKFQKSJOmgoCD6xIkTdgAARqMRNmzY4EWSJE2SJJ2TkzPuSHWdTkdER0cH5ufnu8z+nb0/vb29LKPRSPD5fCMAgLW1NRMSEjLpcfHr16/XREdH9x08ePChZ4cmgzMkCCGExvn/Ci9793TobGazTWdPnv4/UoKmnOEQCARDHA6HaWlpsTxz5oxtZGTkQEdHh0VZWRnPycnJKBKJDFwulzl69Kh9a2srt76+/jLDMBAbGys8efIkb+nSpaNHvRcWFjqp1WpLlUrV2NHRwRGLxeLU1NTR4+hdXFyMTU1Nl3Nzc11zc3P5RUVF7SkpKd08Hs9kngUYa9OmTTdTU1P99+/fr1+yZIl248aNtwUCwVBOTk7H2FmBnTt38gEAmpubmxQKBXfZsmWBbW1tl/bt2+fS3t5u1djY2GRhYTEul41Wq2UlJCT4JyUl3Z4q469UKiXNifdWrVp1a+fOnTenu9cTj4O/H3w+3xQXF9fr4+Mzf/Hixdply5b1bdiwoYfNnjwPX2hoqH5iluV/FgYkCCGEfhTCwsJ05eXltufOneNlZGTcuHbtmmVlZaWtg4ODSSKR6AAASkpK7M+ePWtP0zQNMJIsTqlUcscGJBUVFbz4+HgNm80GHx8fY2Rk5LgMtElJSRoAAIlEoj9+/Pi4/RaTSUhI0EZFRTV8+eWXDiUlJQ5hYWF0Q0ND48R6VVVVvNdff/0mAEBoaOgdDw+Puw0NDdyysjJ7uVzebWExku6Ez+eP5tVZvny5cMuWLV0bN26cctlnqiWbqbz77rvfp6WlacyfzTNHU2UcNn9fVFTUfuHChZsnT56027t371OnT5+2/+tf/3p1smseRdoZDEgQQgiNM91MxqO0cOFCXVVVFU+pVFpHREQY/P397+7Zs4fP4/FMaWlptwBGHoRbtmzpzMjImDLvzkwPSy6XywAAcDgcxmg03te7m3w+3ySXy3vkcnmPTCYTnjp1iufi4jIuYd9U/f6QbG7SwoiICF1JSYnDa6+91mOeBXlU3NzcjH19feOe+xqNhuPq6joa7EgkEoNEIjFs2LChRygUPg0AVydr69tvv7UJCwvTz+b4Hts9JHOUExAhhNAjIpVKdadPn3Z0dHQ0cTgc4PP5Jq1Wy1YoFDyZTDYAALB06VLtoUOHXPr6+lgAAFeuXLHo6OgY95CNjo7WHTt2zMlkMoFareacP3/ebqa+7ezsTP39/ZOuTxw/ftyuv7+fBQCg0WhY7e3tVn5+fncdHBxMYzfeRkVF6Q4fPuwMAFBfX2/V2dlpOX/+/DuxsbHagoIC16GhIQCAcUs2u3fvvu7s7GxMTk72eeAb9oDEYvHgjRs3LC5evMgFAGhubrZUKpXWkZGRhr6+PlZxcfHofTp//ry1h4fH3cna+eMf/+hYUVHh8Oqrr87qZt7HboYEjyFBCKEnk0QiMfT29nLi4+NH91JQFGUYGBhgm5cs4uPjtY2NjdyIiAgKAMDGxmb4888/v+Lp6Tn6K3/t2rWa06dP25EkGezn53cnJCRkwNHR0XRvj/+QkJDQu2LFioCTJ0867tmz59oLL7wwugRUXV1tk56e7sNmsxmGYYjk5ORbUqlUPzg4SHA4HEYkEtFJSUm3tm7dejM5OdmXJEmazWbDgQMHrlpbWzPp6endzc3NVhRFBXM4HGbt2rXdb7/9dre5/YMHD6pXrlwpkMvlXgUFBd9PHNvYPSRBQUH6L7/88urD3F9ra2vm008//S4tLU0wODjI4nA4zAcffNA+b948k0ajYe3evZu/efNmXy6XO2xjYzN88ODBK+ZrCwoK+EeOHJlnMBhYJEka/va3v6k8PDxmNWEh8SjWge5HeHg4U1NT88DX/bHyCuw60QSKzDhwsrWc+QKEEHqCEARRyzBM+Gy3W1dXdzUkJGTKZZDHTV9fH8vBwWG4q6uLHREREVRZWanEjL9zr66uziUkJEQwWdljN0OCEEIIzSQuLi5Qq9Wyh4aGiIyMjE4MRn78MCBBCCH0xLlw4YJqrseAHsxju6kVIYQQQk8ODEgQQgghNOcwIEEIIYTQnMOABCGEEEJz7rENSPBcNIQQenKsW7fOOzs7ezTpXFRUVGBiYqKv+fP69eu9du3axZ+ujdDQUGqmfjw9PZ/u7Oy854WO4uJiu9LSUtvJrlGr1RyZTCYUiUR0QEBAsFQqFQKMZNstKCh46ARzU41lYh2SJGmKomiSJOnDhw87dnV1sSmKoimKol1cXELc3Nzmmz/fuXOHuHbtGufFF1/09/b2FpvHW19fb1VcXGwnk8mEY9tPSEgQmBMP/vnPf3YICgqizX/P3bt3uwAAvPnmmx7mPnx9fcXPP/98QG1tLRcAIC4uLoCiKNrHx0dsZ2f3jHkcU93L6dzXWzYEQbwAAL8DADYA/IFhmNxJ6qwEgF0wEivUMQyT9KCDuc+xPIpmEUIIzaHFixfrvvjiCycAuGkymUCj0XDGnoJaXV3NW7Vq1bRH2isUCuXD9l9WVmbH4/FMcXFxAxPLtm3b5hkTE6PNzMy8CTByiikAQEtLi1VRUZGzXC6f1RNLJzLnsqmrq7NaunQpef369QalUtkEMBIsjE0KODw8DMuXLxcmJSXdLi4u/g4AoKqqyvr69esW0/UxODhI/PKXv/Q9d+7c5YCAgCGDwUA0NzePHvYll8tvmPv4+OOPnX7yk5+I6uvrG0tLS9sARgK6sYkGH8aMMyQEQbAB4AMAWAoANACsIgiCnlAnEAD+GwAWMwwTDABbHnZACCGE/v3ExMToamtreQAAtbW11iKRyGBra2vq7u5mGwwGoq2tjbto0SI9AEBmZiZfLBYHkSRJp6ene5jbMCeRM5lMsGbNGh+hUBgsk8mEUqlUaJ4FAADIy8tzo2k6iCRJWqFQcFUqlWVhYaFrQUEBn6IouqSkhDd2bF1dXRbe3t6jx6gvWLDAAACwfft2z5qaGh5FUXRWVpabXq8nVqxYISBJkg4KCqJPnDhhBwBgNBphw4YNXiRJ0iRJ0jk5OW5j29fpdER0dHRgfn6+y3T3qLe3l21vbz/tibPFxcV2HA6H2bp16+hJsIsWLTKMPXl2irZZRqOR4PP5RoCRU11DQkIGJ6u7fv16TXR0dN/BgwcfenZoMvczQyIBgFaGYb4DACAI4i8A8J8A0DR2fADwAcMwGgAAhmGmTY2MEELox+tv+/d431K328xmmy7evvqfbNwy5QyHQCAY4nA4TEtLi+WZM2dsIyMjBzo6OizKysp4Tk5ORpFIZOByuczRo0ftW1tbufX19ZcZhoHY2FjhyZMneWOz/RYWFjqp1WpLlUrV2NHRwRGLxeLU1NTR4+hdXFyMTU1Nl3Nzc11zc3P5RUVF7SkpKd1jZxrG2rRp083U1FT//fv365csWaLduHHjbYFAMJSTk9MxdlZg586dfACA5ubmJoVCwV22bFlgW1vbpX379rm0t7dbNTY2NllYWIzLZaPValkJCQn+SUlJtzdv3nx7Yt8AI0fHMwxDfP/995affPLJd9Pd5/r6euuQkJAHTnrH5/NNcXFxvT4+PvMXL16sXbZsWd+GDRt62OxJ0/tAaGioXqlUch+0n+nczx4STwAY+4/o+x++G4sEAJIgiEqCIL75YYnnHgRBbCAIooYgiJru7u7JqiCEEPo3FRYWpisvL7c9d+4cLzo6Wrdo0aKByspK24qKCp5EItEBAJSUlNifPXvWnqZpOjg4mG5ra+NOfDBWVFTw4uPjNWw2G3x8fIyRkZH9Y8uTkpI0AAASiUSvVqutZhpXQkKCtrW1tSEtLe2WSqWyDgsLo69fv37PD/qqqipeSkrKbQCA0NDQOx4eHncbGhq4ZWVl9nK5vNvCYmTVhM/nj85yLF++XJicnHxrqmAEYGTJpqWlpbGmpqbprbfe8jEnFnxQU2UcNn9fVFTUXlJS0hweHj6wd+/ep1auXCmYqq1HkXbmfmZIJtu0MXEkHAAIBIAlAOAFABUEQYgZhukddxHDfAQAHwGM5LJ54NEihBB65KabyXiUFi5cqKuqquIplUrriIgIg7+//909e/bweTyeKS0t7RbAyINwy5YtnRkZGVPm3ZnpYcnlchkAAA6HwxiNxvvamMjn801yubxHLpf3yGQy4alTp3guLi7jlk+m6pdhmCmDgYiICF1JSYnDa6+91mNOoDeV4ODgwXnz5g1dvHiRK5PJJp0Fefrppw3Hjh1zmqzMzc3N2NfXN+65r9FoOK6urqPH6kskEoNEIjFs2LChRygUPg0AVydr69tvv7UJCwt74JmY6dxPlPU9AHiP+ewFANcnqfO/DMMMMQxzBQBUMBKgIIQQQvdFKpXqTp8+7ejo6GjicDjA5/NNWq2WrVAoeDKZbAAAYOnSpdpDhw65mGcJrly5YtHR0THuIRsdHa07duyYk8lkArVazTl//rzdTH3b2dmZ+vv7J12fOH78uF1/fz8LAECj0bDa29ut/Pz87jo4OJjGbryNiorSHT582BkAoL6+3qqzs9Ny/vz5d2JjY7UFBQWuQ0NDAADjlmx279593dnZ2ZicnOwz0xg7Ojo433//vZVQKLw7VZ2f/exn/Xfv3iXG7kc5c+aMzVdffcUTi8WDN27csLh48SIXAKC5udlSqVRaR0ZGGvr6+ljFxcWj9+n8+fPWHh4ek/bzxz/+0bGiosLh1VdfndXNvPczQ1INAIEEQfgBQAcAvAIAE9+gOQYAqwDgjwRBuMDIEs6061wIIYTQWBKJxNDb28uJj48fXb6gKMowMDDAdnd3NwIAxMfHaxsbG7kREREUAICNjc3w559/fsXT03P0V/7atWs1p0+ftiNJMtjPz+9OSEjIgKOj47SbQRMSEnpXrFgRcPLkScc9e/ZcG7sJtLq62iY9Pd2HzWYzDMMQycnJt6RSqX5wcJDgcDiMSCSik5KSbm3duvVmcnKyL0mSNJvNhgMHDly1trZm0tPTu5ubm60oigrmcDjM2rVru99+++3RfQsHDx5Ur1y5UiCXy70KCgq+nzg2qVRKslgsMBqNxI4dO7739vaeMlEgi8WC48ePt/3iF7/w3rNnz1NWVlaMl5fX4L59+9TW1tbMp59++l1aWppgcHCQxeFwmA8++KB93rx5Jo1Gw9q9ezd/8+bNvlwud9jGxmb44MGDV8ztFhQU8I8cOTLPYDCwSJI0/O1vf1N5eHjMasJC4n7WgQiCWAYAe2Dktd9PGIbJIQgiGwBqGIY5Toy8i5sPAC8AgAkAchiG+ct0bYaHhzM1NTUPPODPqq7CzuONUPtOLMzjzbj0hxBCTxSCIGoZhgmf7Xbr6uquhoSETLkM8rjp6+tjOTg4DHd1dbEjIiKCKisrlZjxd+7V1dW5hISECCYru69zSBiG+RoAvp7w3Y4xf2YA4M0f/nuk8BgShBBCM4mLiwvUarXsoaEhIiMjoxODkR+/+wpIEEIIocfJhQsXVHM9BvRgHtuj4xFCCCH05MCABCGEEEJzDgMShBBCCM05DEgQQgghNOcwIEEIITTn1q1b552dnT2adC4qKiowMTHR1/x5/fr1Xrt27eJP10ZoaCg1Uz+enp5Pd3Z23vNCR3FxsV1paantZNeo1WqOTCYTikQiOiAgIFgqlQoBAFQqlWVBQcFDJ5ibaixmGo2G5e3tLW5oaLACGMnIS5IkXVZWZgsAwGazwyiKogMDA4NjYmKEt27dYpvHxeVyn6Uoig4ICAhOSkryMZlM474XiUR0aGgoVVdXZ/XXv/7VnqIomqIo2sbGJlQgEIgpiqJfeuklAQBAeXm5TXh4uEggEIj9/PyCExMTfc0Hxc2mxzYgwXPnEULoybF48WLdN998wwMYydar0Wg4KpXK2lxeXV3Ne+6556bNWKtQKJQP239ZWZldRUUFb7Kybdu2ecbExGhVKlVTW1tbY15eXgcAQEtLi1VRUdGsZrwdy8nJaXjnzp0dcrncBwBg165d/PDw8IGYmJgBAAArK6thpVLZ1NLS0ujo6GjcvXu3q/lab2/vQaVS2aRUKhubm5utDx8+7Dj2e5VK1ZSUlHQrKyvLPSEhQftD3SaxWKwvLCz8TqlUNn355ZdX1Wo1Z/Xq1QG5ubnfX7169VJbW1vjCy+8oO3t7cWABI8hQQihJ09MTIyutraWBwBQW1trLRKJDLa2tqbu7m62wWAg2trauIsWLdIDAGRmZvLFYnEQSZJ0enq6h7kNGxubUICRgGbNmjU+QqEwWCaTCaVSqfDTTz8dze+Sl5fnRtN0EEmStEKh4KpUKsvCwkLXgoICPkVRdElJybjApKury8Lb23v0GPUFCxYYAAC2b9/uWVNTw6Mois7KynLT6/XEihUrBCRJ0kFBQfSJEyfsAACMRiNs2LDBiyRJmiRJOicnx21s+zqdjoiOjg4ce9y72X/9139pWCwWvPPOO/zPPvvM9be//e09J7kCAPyQHdly4vcWFhYgkUh0LS0t95wkqtVq2TOdYJufn++2cuXK27GxsQMAIyfBpqWlaaY7LfZh4TkkCCGExun5otl7qGvAZjbbtHjKVu+8gpwyaZ9AIBjicDhMS0uL5ZkzZ2x/eMBalJWV8ZycnIwikcjA5XKZo0eP2re2tnLr6+svMwwDsbGxwpMnT/KWLl06OntSWFjopFarLVUqVWNHRwdHLBaLU1NTR4+jd3FxMTY1NV3Ozc11zc3N5RcVFbWnpKR083g8U3Z29o2JY9u0adPN1NRU//379+uXLFmi3bhx422BQDCUk5PTkZ+fzy8vL28FANi5cycfAKC5ublJoVBwly1bFtjW1nZp3759Lu3t7VaNjY1NFhYW43LZaLVaVkJCgn9SUtLtqTL+7t27V/3ss88G/+Y3v2kfmynYzGg0Qnl5ud26devuOWm3v7+fdfbsWfsdO3Z0AACo1WoriqLogYEB1p07d1hVVVXTzio1NTVZmzMYP2qP3QwJQgihJ1NYWJiuvLzc9ty5c7zo6GjdokWLBiorK20rKip4EolEBwBQUlJif/bsWXuapung4GC6ra2Nq1QquWPbqaio4MXHx2vYbDb4+PgYIyMj+8eWJyUlaQAAJBKJXq1Wz5iDJCEhQdva2tqQlpZ2S6VSWYeFhdHXr1+/5wd9VVUVz/zwDg0NvePh4XG3oaGBW1ZWZi+Xy7stLCwAYCRzsPma5cuXC5OTk29NFYwAAJw4ccLe1dV16NKlS9Zjvx8cHGRRFEU7OTk909vby/n5z3+uNZeZAw+JREI9//zzfStXrtQC/GPJRq1WX3rvvffUr776qu/E/uYKzpAghBAaZ7qZjEdp4cKFuqqqKp5SqbSOiIgw+Pv7392zZw+fx+OZ0tLSbgEAMAwDW7Zs6czIyJgy785MOdq4XC4DAMDhcBij0XhfOwH4fL5JLpf3yOXyHplMJjx16hTPxcVl3GzFVP0yDAMEQUxaGBERoSspKXF47bXXelise+cIrl69avHRRx/xz507dzkmJkZ0/vz5W+YlI/Mektu3b7Off/55YW5urts777xzE+Afgcd0f6dVq1b1vvHGG4Lp6gQFBRlqamps1qxZ0ztdvdmAMyQIIYR+FKRSqe706dOOjo6OJg6HA3w+36TVatkKhYInk8kGAACWLl2qPXTokEtfXx8LAODKlSsWHR0d435cR0dH644dO+ZkMplArVZzzp8/bzdT33Z2dqb+/n72ZGXHjx+3M79VotFoWO3t7VZ+fn53HRwcTDqdbvSaqKgo3eHDh50BAOrr6606Ozst58+ffyc2NlZbUFDgOjQ0BAAwbslm9+7d152dnY3Jyck+k/W9adMm7zfffLMzICBg6H/+53/Uv/jFL3yGh4fH1Zk3b55p79691z744AP+4ODgfW+1LC0ttfP29h6crs5bb71188iRI/PMb/YAAHz44YfO165dm/UJDQxIEEII/ShIJBJDb28vJzw8fHQ/CEVRBh6PZ3J3dzcCAMTHx2tffvnlnoiICIokSfqll14K6O3tHRdIrF27VuPu7n6XJMngtLQ035CQkIGZNm8mJCT0fvXVV46TbWqtrq62eeaZZ4JIkqQlEklQcnLyLalUqpdIJAYOh8OIRCI6KyvLbevWrTdNJhNBkiSdmJgYcODAgavW1tZMenp6t5eX112KooJFIhF98ODBcW/mHDx4UD04OMiSy+VeY7//8ssv7Ts6Oiy3bNlyCwAgKSmpz8HBwfTBBx/Mmzj+xYsXG4KCggx/+MMfnCaWjWVeyhGJRBatp/gAACAASURBVHRmZqZnQUFB+3T1vb29jYWFhd9lZGR4CQQCsb+/f/Df//53Oycnp+HprnsYxExTW49KeHg4U1NT88DXHTp3FTL/txFq3okFF96MS38IIfREIQiilmGY8Nlut66u7mpISMiUyyCPm76+PpaDg8NwV1cXOyIiIqiyslKJGX/nXl1dnUtISIhgsjLcQ4IQQuiJExcXF6jVatlDQ0NERkZGJwYjP36PbUAyRxM7CCGEHgMXLlxQzfUY0IN5/PaQEHg0GkIIIfSkefwCEoQQQgg9cTAgQQghhNCcw4AEIYQQQnMOAxKEEEJzbt26dd7Z2dmjSeeioqICExMTR481X79+vdeuXbv407URGhpKzdSPp6fn052dnfe80FFcXGxXWlpqO9k1arWaI5PJhCKRiA4ICAiWSqVCAACVSmVZUFDw0Nl+pxrLxDrmpHwBAQHBb7zxhofBYBi3mTIrK8vNysrq2du3b4+ex9Lf389avny5H0mSdGBgYHBYWJjIfJicOQmh2d69e+elpKT4AAC8+eabHm5ubvMpiqL9/PyCV69e7WMymSA5OdmHoig6ICAgmMvlPktRFE1RFG1OWrhjxw6+n59fcGBgYLBIJKJ///vf33NWykwwIEEIITTnFi9erPvmm294ACPZejUaDUelUo3mbqmuruY999xzuqlbAFAoFNMmiptOWVmZXUVFBW+ysm3btnnGxMRoVSpVU1tbW2NeXl4HAEBLS4tVUVHRQwck9+vMmTPNzc3NTRcvXrx85coVq9WrV4/LP/PFF1/ME4vFA59//rmj+bv33nvPzc3Nbai5ubmppaWl8ZNPPrlqaWl5X++nyuXyG0qlsqm1tbVRqVRaf/3113aHDh26plQqm77++usW87H0SqWyKS0tTZOXl+daVlZmX1tbe7mlpaWxqqpK9TBnnGFAghBCaM7FxMToamtreQAAtbW11iKRyGBra2vq7u5mGwwGoq2tjbto0SI9AEBmZiZfLBYHkSRJp6ene5jbMP/yN5lMsGbNGh+hUBgsk8mEUqlUaP4lDwCQl5fnRtN0EEmStEKh4KpUKsvCwkLXgoIC/mQntXZ1dVl4e3vfNX8255LZvn27Z01NDY+iKDorK8tNr9cTK1asEJAkSQcFBdEnTpywAxjJxrthwwYv80xHTk6O29j2dTodER0dHZifn+8y3T1ycHAY/uyzz9pLS0sdzcfPNzY2Wun1elZ2dnbHkSNHRoOjzs5OC09PzyHz55CQkEFra+sHihIGBweJwcFB1rx586Y9w+W3v/3tUwcOHLjm7Ow8DDBylP3rr7/+wBmCH99zSAAPIkEIoUfh2LFj3jdv3rSZzTbd3Nz0P//5z6dM2icQCIY4HA7T0tJieebMGdvIyMiBjo4Oi7KyMp6Tk5NRJBIZuFwuc/ToUfvW1lZufX39ZYZhIDY2Vnjy5Ene0qVLR2dPCgsLndRqtaVKpWrs6OjgiMVicWpq6ugD0sXFxdjU1HQ5NzfXNTc3l19UVNSekpLSzePxTNnZ2Tcmjm3Tpk03U1NT/ffv369fsmSJduPGjbcFAsH/396dx0V53fsD/5xZmAGGVWBEQFmHYTAissS4EYzmam6bVjE18QaVm8RgY3o1qZo0dUuvqTGx8TbJjaa1vVGbxDQ3MWazJj+set2lBFEEgSgii2wDw8Awy/Oc3x8zQwCHxY3Fft+vFy9mnvX7nFme75znPOdYN27cWLllyxb1wYMHSwFg3bp1agC4ePFiYV5envKhhx6KKSsrO/fmm28GlJeXK86fP18ol8u7jGVjMBgkGRkZkQsWLGjobcRfJ39/fzEkJMRy/vx5pVqtbn3vvff8586d2zhr1izjkiVLlJWVlbKQkBDbkiVL6n/0ox9pPvvsM79p06YZnnrqqYZ77rnHDPwwSrBzm83NzdKZM2c2O59v27ZN/dFHH42oqqpyS0tLa540aZKpp3j0er2ktbVVGh8f3+uYOP0x7GpIqBcSQgi5OyUlJRkPHjzoefz4cdXUqVONkyZNaj169KjnkSNHVKmpqUYA2L9/v/fhw4e9dTqdLj4+XldWVqYsKipSdt7OkSNHVHPnztVLpVKMHj3aNnHixJbO8xcsWKAHgNTU1LaKioo+xyDJyMgwlJaWFmRlZdUXFxe7JyUl6aqqqq77QX/s2DHVwoULGwAgMTGxfdSoUZaCggJlTk6Od3Z2dp1cLgdgHznYuc7DDz8cnZmZWd+fZMSp8+WQTz/91H/hwoWNUqkUs2fP1u/cudMPACZNmmS6dOlSwYoVK2oaGxtlkyZNivvHP/6hBH4YJdj59+KLL1Z13r7zkk1dXV1+W1ub5N133+1xfBzHSMb9Db1Xw7aGhBBCyJ3RW03GnXTfffcZjx07pioqKnJPSUkxRUZGWrZu3apWqVRCVlZWPWA/AS5fvrx65cqVPY6701f7BaVSyQFAJpNxm83Wr7OpWq0WsrOzG7OzsxvT09OjDxw4oAoICOgyYF9P+3WctF3OTElJMe7fv9/n6aefbpRI+q4j0Ov1kqqqKrd77rmn/eTJk+7l5eWKWbNmaQDAarWysLAw84svvlgH2C/xLFq0qGnRokVNCxcuxGeffeYzYcKE9v4cLwAoFAr+4IMPGg4fPuy1ZMkSvatl/P39RXd3d7GwsNBNp9NZXC3TX8OuhoQQQsjdKS0tzfjtt9/6+vr6CjKZDGq1WjAYDNK8vDxVenp6KwDMnj3bsGvXrgDnHSOXLl2SV1ZWdvlxPXXqVOPevXv9BEFARUWF7OTJk1597dvLy0toaWmRupq3b98+r5aWFglgTwjKy8sVERERFh8fH8FoNHasM2XKFOPu3bv9AeDs2bOK6upqt3HjxrXPmDHDsG3btkCr1d6ko/Mlm9dee63K39/flpmZObqvGJubmyVZWVljZs6c2RQYGCjs3LnT//nnn6+qrKwsqKysLKitrT1bU1PjdvHiRbcDBw541tXVSQGgvb2dXbx4URkeHn5DCYMoijh+/LgqKiqq18sxy5cvr87Ozh7T2NgoAYDGxkbJ66+/3mt7GFcoISGEEDIkpKammpqammTJyckd7UG0Wq1JpVIJwcHBNgCYO3eu4ZFHHmlMSUnRajQa3Zw5c6Kampq6JBKLFi3SBwcHWzQaTXxWVtaYhISEVl9fX6H7/jrLyMho+vLLL31dNWo9ffq0x/jx4+M0Go0uNTU1LjMzsz4tLa0tNTXVJJPJeGxsrG7Dhg1Bq1atqhUEgWk0Gt38+fOjtm/fftnd3Z2vWLGiLjQ01KLVauNjY2N1O3bs6HJnzo4dOyrMZrMkOzs71FVsaWlpmpiYmPgJEybEhYWFWXbv3l0OAHv37vX/2c9+1tR52dmzZ+vfe+89/4sXLyonT54cq9FodGPHjtWNHz++bdGiRS5rObpzNu7VaDTxgiBg5cqVtb0tv2rVqrpp06YZJkyYoIuJiYmfPHmy1sPDQ+zPvjpjN3Nrzu2QnJzMz5w5c8Pr7T5Rjl/vPYdTLz2AIC9l3ysQQshdhDGWyzlPvt3bzc/Pv5yQkNDjZZDhprm5WeLj4yPW1NRIU1JS4o4ePVpEI/4Ovvz8/ICEhIRwV/OoDQkhhJC7zsyZM2MMBoPUarWylStXVlMyMvT1KyFhjM0C8F8ApAD+yDnf1MNy8wD8FUAK5/zGqz8IIYSQ2+DUqVPFgx0DuTF9tiFhjEkBvA1gNgAdgMcYYzoXy3kB+AWAk7c7SEIIIYTc3frTqDUVQCnn/HvOuQXAhwB+4mK53wDYDKDftxTdEuoXjRBCCLlr9CchCQHQ+Z70q45pHRhjiQDCOOdf3MbYXLpN/a8QQgghZAjpT0LiKgXoqJ9gjEkAvAHg+T43xNgSxtgZxtiZurq6/kdJCCGEkLtafxKSqwDCOj0PBdC5m1kvAGMB/J0xdhnARAD7GGPX3ZbGOX+Xc57MOU8ODAy8+agJIYTcVZ544omwl19+uWPQuSlTpsTMnz+/Y1Tbp556KnT9+vXq3raRmJio7Ws/ISEh91RXV193Q8cXX3zh9c0333i6WqeiokKWnp4eHRsbq4uKiopPS0uLBoDi4mK3bdu23fRovz3F4qTX6yVhYWFjCwoKFIB9sDuNRqPLycnxBADGWNJPf/rTCOfyVqsVfn5+Cenp6dEA8Pvf/36En59fglar1UVERMRv2LCho3yfe+65UUFBQeOc8/7t3/5ttCAIyMzMHK3VanVRUVHxSqVyglar1Wm1Wp1zcMK1a9eqIyIi4mNiYuJjY2N1b7311oibPf7u+pOQnAYQwxiLYIy5AXgUwD7nTM55M+c8gHMezjkPB3ACwMN0lw0hhJD+mjx5svHEiRMqwD5ar16vlxUXF7s7558+fVo1bdo0Y89bAPLy8opudv85OTleR44cUbmat3r16pDp06cbiouLC8vKys5v3ry5EgBKSkoUe/bsuemEpC9+fn7iunXrKrOzs0cDwPr169XJycmt06dPbwUAd3d3sbi42N1oNDIA+PTTT73VarW18zZ+/OMf64uKigqPHz9etHXr1uDS0lK5c55zzJrS0tLzRUVF7l999ZXXrl27rhQVFRV+9dVXJWFhYWbneDdZWVn6zZs3B+bk5Hjn5uZeKCkpOX/s2LHi29mXWZ8JCefcBmAZgL8BuADgI875ecbYy4yxh29bJIQQQv5pTZ8+3Zibm6sCgNzcXPfY2FiTp6enUFdXJzWZTKysrEw5adKkNgBYs2aNeuzYsXEajUa3YsWKUc5teHh4JAL2hObxxx8fHR0dHZ+enh6dlpYW7fyFDwCbN28O0ul0cRqNRpeXl6csLi5227lzZ6Czh9LuPbXW1NTIw8LCOrpdv/fee00A8NJLL4WcOXNGpdVqdRs2bAhqa2tj8+bNC9doNLq4uDjd559/7gUANpsNS5YsCdVoNDqNRqPbuHFjUOftG41GNnXq1JgtW7Zc1936k08+qZdIJPj1r3+tfu+99wLfeOONq53nP/DAA81//etffQHggw8+8M/IyGh0Vb4jR44URo8eba6oqJB3n2c2m5nZbJaMGDGi175a3njjjZHbt2+/4u/vLwLAiBEjhGeffbbfgwL2pV/9kHDOvwLwVbdpa3tY9v5bD4sQQshgKbywOqzVeNHjdm7TU6Vp08W92uOgfeHh4VaZTMZLSkrcDh065Dlx4sTWyspKeU5OjsrPz88WGxtrUiqV/JNPPvEuLS1Vnj179gLnHDNmzIj++uuvVbNnz+6oPdm5c6dfRUWFW3Fx8fnKykrZ2LFjxy5evLjjxBkQEGArLCy8sGnTpsBNmzap9+zZU75w4cI6lUolvPzyy9e6x/bMM8/ULl68OPKdd95pu//++w1Lly5tCA8Pt27cuLFyy5Yt6oMHD5YCwLp169QAcPHixcK8vDzlQw89FFNWVnbuzTffDCgvL1ecP3++UC6XdxnLxmAwSDIyMiIXLFjQ0NOIv7///e8rJkyYEP/666+Xdx4pGAAyMzMb161bFzx//vymCxcueDzxxBMNx44du66mp6SkxM1sNkucyRRg7yL+o48+GlFVVeWWlpbWPGnSJFP39Zz0er2ktbVVGh8f3+u4NreCxrIhhBAyJCQlJRkPHjzoefz4cdXUqVONkyZNaj169KjnkSNHVKmpqUYA2L9/v/fhw4e9dTqdLj4+XldWVqYsKirqMo7IkSNHVHPnztVLpVKMHj3aNnHixJbO8xcsWKAHgNTU1LaKigpFX3FlZGQYSktLC7KysuqLi4vdk5KSdFVVVdf9oD927Jhq4cKFDQCQmJjYPmrUKEtBQYEyJyfHOzs7u04ut1dOdE4qHn744ejMzMz6npIRAPj888+9AwMDrefOnXPvPu/ee+81Xb16VfGHP/zBf8aMGc0u1vWLjo6Oj4uLu2fp0qXXPDw8Oq6xOC/Z1NXV5be1tUneffddv+7rOzlGLO6llG7dsO06nrohIYSQO6O3mow76b777jMeO3ZMVVRU5J6SkmKKjIy0bN26Va1SqYSsrKx6wH5iXL58efXKlSt7HHenr3YNSqWSA4BMJuM2m61fZ1m1Wi1kZ2c3ZmdnN6anp0cfOHBAFRAQ0KW2oqf9Ok7mLmempKQY9+/f7/P00083SiTX1xFcvnxZ/u6776qPHz9+Yfr06bEnT56s71zLAQCzZs1qWrduXdiBAweKa2tru5zXf/zjH+t37tx55dtvv/XMyMiImTNnTnP3bvQVCgV/8MEHDYcPH/ZasmSJywH4/P39RXd3d7GwsNBNp9Pd0KjB/TXsakiYy7uQCSGEDHdpaWnGb7/91tfX11eQyWRQq9WCwWCQ5uXlqdLT01sBYPbs2YZdu3YFNDc3SwDg0qVL8srKyi4n4alTpxr37t3rJwgCKioqZCdPnvTqa99eXl5CS0uL1NW8ffv2ebW0tEgA+6WL8vJyRUREhMXHx0cwGo0d60yZMsW4e/dufwA4e/asorq62m3cuHHtM2bMMGzbti3QarW3N+18yea1116r8vf3t2VmZo52te9nnnkm7LnnnquOioqy/va3v634+c9/PloUuw6ku3Tp0vrnn3++KjU1tcdLLjNmzGidO3duw6uvvnrdnUqiKOL48eOqqKioXi/HLF++vDo7O3tMY2OjBAAaGxslr7/++nXtXm7WsEtICCGE3J1SU1NNTU1NsuTk5I72IFqt1qRSqYTg4GAbAMydO9fwyCOPNKakpGg1Go1uzpw5UU1NTV0SiUWLFumDg4MtGo0mPisra0xCQkKrr6+v0H1/nWVkZDR9+eWXvq4atZ4+fdpj/PjxcRqNRpeamhqXmZlZn5aW1paammqSyWQ8NjZWt2HDhqBVq1bVCoLANBqNbv78+VHbt2+/7O7uzlesWFEXGhpq0Wq18bGxsbodO3Z0uTNnx44dFWazWZKdnR3aefqnn37qXVlZ6bZ8+fJ6AFiwYEGzj4+P8Pbbb3e51TYqKsq6Zs2a2r7Kd926dTV79uwJ0Ov1EsDehkSr1eo0Gk28IAhYuXJlr9tYtWpV3bRp0wwTJkzQxcTExE+ePFnr4eEh9rbOjWC385adG5GcnMzPnLnxO4PfP3kFv/q0ACd/9QDU3sq+VyCEkLsIYyyXc35dP0+3Kj8//3JCQkKPl0GGm+bmZomPj49YU1MjTUlJiTt69GgRjfg7+PLz8wMSEhLCXc0btm1ICCGEkJ7MnDkzxmAwSK1WK1u5cmU1JSNDHyUkhBBC7jqnTp0qHuwYyI2hNiSEEEIIGXSUkBBCCCFk0A3bhGSQ2uISQggh5A4YdgnJHe4ojhBCCCGDYNglJIQQQu4+TzzxRNjLL7/cMejclClTYubPnz/G+fypp54KXb9+/XWdenWWmJio7Ws/ISEh91RXV193Q8cXX3zh9c0333i6WqeiokKWnp4eHRsbq4uKiopPS0uLBoDi4mK3bdu23fRovz3F0tsyv/vd7wK0Wq1Oq9Xq5HL5BI1Go9Nqtbpnn302BAA+/PBDn/j4+LioqKj4iIiI+KVLl4YAwC9+8YtRQUFB47RarS4yMjI+MzNztCAIWLBgwWitVquLioqKVyqVE5zb3rlzp68oivj1r3+tDg8PHxsTExOv1Wp177zzzh0b3ZjusiGEEDLoJk+ebPz444/9ANQKggC9Xi/r3Avq6dOnVY899livXdrn5eUV3ez+c3JyvFQqlTBz5szW7vNWr14dMn36dIOz87GTJ0+6A0BJSYliz549/tnZ2S5H2L0TnnvuufrnnnuuHgDUavW4Y8eOFTu7sD9+/Lj76tWrwz7//POScePGma1WK373u98FOtddtmxZzdq1a2ttNhuSkpK033zzjer999+/AgDnzp1TzJs3L6qoqKjQufwrr7wSePjwYe+8vLxCPz8/sb6+Xvrhhx/63qljoxoSQgghg2769OnG3NxcFQDk5ua6x8bGmjw9PYW6ujqpyWRiZWVlykmTJrUBwJo1a9Rjx46N02g0uhUrVoxybsPDwyMRAARBwOOPPz46Ojo6Pj09PTotLS36z3/+c8fAcZs3bw7S6XRxGo1Gl5eXpywuLnbbuXNnoLPn0u49tdbU1MjDwsI6xm9xjiXz0ksvhZw5c0al1Wp1GzZsCGpra2Pz5s0L12g0uri4ON3nn3/uBQA2mw1LliwJ1Wg0Oo1Go9u4cWNQ5+0bjUY2derUmC1bttxSN+yvvPLKyJUrV1aPGzfODAByuRyrV6+u675ce3s7s1gsEn9//157r926dWvwH//4x3I/Pz8RAAICAoTeBgG8VVRDQgghpIvlF66EFbW2e9zObWo9lW1b40b3WMMRHh5ulclkvKSkxO3QoUOeEydObK2srJTn5OSo/Pz8bLGxsSalUsk/+eQT79LSUuXZs2cvcM4xY8aM6K+//lo1e/bsju7md+7c6VdRUeFWXFx8vrKyUjZ27Nixixcv7jiRBgQE2AoLCy9s2rQpcNOmTeo9e/aUL1y4sE6lUgkvv/zyte6xPfPMM7WLFy+OfOedd9ruv/9+w9KlSxvCw8OtGzdurNyyZYv64MGDpQCwbt06NQBcvHixMC8vT/nQQw/FlJWVnXvzzTcDysvLFefPny+Uy+VdxrIxGAySjIyMyAULFjTc6sm+uLjYfd26ddU9zX/rrbdGvv/++wFVVVVuDzzwQFNvY9/U1dVJrVYr02g0d2QgPVeohoQQQsiQkJSUZDx48KDn8ePHVVOnTjVOmjSp9ejRo55HjhxRpaamGgFg//793ocPH/bW6XS6+Ph4XVlZmbKoqKjLOCJHjhxRzZ07Vy+VSjF69GjbxIkTWzrPX7BggR4AUlNT2yoqKhR9xZWRkWEoLS0tyMrKqi8uLnZPSkrSVVVVXfeD/tixY6qFCxc2AEBiYmL7qFGjLAUFBcqcnBzv7OzsOrlcDsA+crBznYcffjg6MzOz/k7WPDgtW7aspqioqLC2tjZfr9fLOtcaddd9AL+BQDUkhBBCuuitJuNOuu+++4zHjh1TFRUVuaekpJgiIyMtW7duVatUKiErK6seADjnWL58efXKlSt7HHenrzHalEolBwCZTMZtNlu/7t1Uq9VCdnZ2Y3Z2dmN6enr0gQMHVM62G33tl3MOxpjLmSkpKcb9+/f7PP30040Sya3VEcTGxppOnDjhmZyc3N7bckqlks+cOdNw6NAhVVZWlt7VMmq1WpDL5bykpMQtJiZmQGpJqIaEEELIkJCWlmb89ttvfX19fQWZTAa1Wi0YDAZpXl6eKj09vRUAZs+ebdi1a1dAc3OzBAAuXbokr6ys7PLjeurUqca9e/f6CYKAiooK2cmTJ7362reXl5fQ0tIidTVv3759Xi0tLRIA0Ov1kvLyckVERITFx8dH6NzwdsqUKcbdu3f7A8DZs2cV1dXVbuPGjWufMWOGYdu2bYFWqxUAulyyee2116r8/f1tmZmZo2+4wLp54YUXal5//fXgc+fOKQB72xVXdyaJoogTJ054RkVFmXvb3i9+8YuaJUuWjHaODlxfXy+91XYuvRm2CQkH9YxGCCF3k9TUVFNTU5MsOTm5oz2IVqs1qVQqITg42AYAc+fONTzyyCONKSkpWo1Go5szZ05UU1NTl0Ri0aJF+uDgYItGo4nPysoak5CQ0Orr69trA86MjIymL7/80tdVo9bTp097jB8/Pk6j0ehSU1PjMjMz69PS0tpSU1NNMpmMx8bG6jZs2BC0atWqWkEQmEaj0c2fPz9q+/btl93d3fmKFSvqQkNDLVqtNj42Nla3Y8eOLrfO7tixo8JsNkuys7NDXcWWkJCgU6vV49Rq9bgnn3zS5TIAMHnyZNPGjRsrfvazn0VGRkbGx8bGxtfV1XUka2+99dZIrVar02g08RKJBM8///x1DV47+9WvflU7ceJEY2Jioi4mJiZ+6tSpsSqV6o5dy2F9VW3dKcnJyfzMmTM3vN6Hp67ghU8KcPzF6Qj2cb8DkRFCyNDFGMvlnCff7u3m5+dfTkhI6PEyyHDT3Nws8fHxEWtqaqQpKSlxR48eLaIRfwdffn5+QEJCQriredSGhBBCyF1n5syZMQaDQWq1WtnKlSurKRkZ+ighIYQQctc5depU8WDHQG7MsG1DQgghhJC7ByUkhBBCCBl0lJAQQgghZNBRQkIIIYSQQTdsG7UO0t3KhBBC7oAnnngibMyYMea1a9fWAsCUKVNiQkJCLHv27CkHgKeeeio0JCTEun79+uvGmnFKTEzU9jXib0hIyD1nzpy54OzXxOmLL77wUigUoqvRfisqKmQLFy4Mr6qqcrPZbCw0NNR86NCh0uLiYreDBw+qbna0355i6b6Mp6en4OzFdeLEiS2CILDTp0+rrFYrq6ysVISHh7cDwOrVq6uzsrL0a9euVe/atStAJpNBIpHwZ5999tqyZcsaUlNTY2tra+VKpVIEgPDw8PaEhIS2zz77zB8ASkpK3GNiYkwA8Pjjj9c3NjbKuo/v05+Yb9awS0hYvzr5JYQQMpxMnjzZ+PHHH/sBqBUEAXq9Xta5F9TTp0+rHnvssV67tO8rGelNTk6Ol0qlElwlJKtXrw6ZPn26Yc2aNbUAcPLkSXcAKCkpUezZs8f/ZhOS/jp06NBFVwlAcXGx249+9KOYoqKiQue0zZs3B+bk5Hjn5uZe8Pf3FxsaGqTvv/++r3P+zp07v582bVpb5+28+uqrNYB9tOTO23ruuedGYQDRJRtCCCGDbvr06cbc3FwVAOTm5rrHxsaaPD09hbq6OqnJZGJlZWXKSZMmtQHAmjVr1GPHjo3TaDS6FStWdJw0PTw8EgFAEAQ8/vjjo6Ojo+PT09Oj09LSojsPJLd58+YgnU4Xp9FodHl5ecri4mK3nTt3Bm7btk3tqqfWmpoaeVhYWMd4Lvfee68JAF566aWQM2fOqLRarW7Dhg1BbW1tbN68eeEajUYXFxen+/zzz70AexfuS5YstXodYwAAIABJREFUCdVoNDqNRqPbuHFjUOftG41GNnXq1Jjb0S37G2+8MXL79u1X/P39RQAYMWKE8Oyzz97xgftuh2FXQ0IIIeTOWvlxftjFmhaP27lNzUivttfmJfRYwxEeHm6VyWS8pKTE7dChQ54TJ05srayslOfk5Kj8/PxssbGxJqVSyT/55BPv0tJS5dmzZy9wzjFjxozor7/+WjV79uyO7uZ37tzpV1FR4VZcXHy+srJSNnbs2LGLFy/uOCkHBATYCgsLL2zatClw06ZN6j179pQvXLiwrvvlCadnnnmmdvHixZHvvPNO2/33329YunRpQ3h4uHXjxo2VW7ZsUR88eLAUANatW6cGgIsXLxbm5eUpH3rooZiysrJzb775ZkB5ebni/PnzhXK5vMtYNgaDQZKRkRG5YMGChp5G/E1LS9M4L9k89thj9evWrat1tZxer5e0trZK4+PjexyjZuHChZHOSzZpaWmG7du3X+1pWQDYtm2b+qOPPhrhfF5bWyvvbflbQQkJIYSQISEpKcl48OBBz+PHj6tWrlx57cqVK25Hjx719PHxEVJTU40AsH//fu/Dhw9763Q6HQC0tbVJioqKlJ0TkiNHjqjmzp2rl0qlGD16tG3ixIktnfezYMECPQCkpqa27du3zw99yMjIMEyZMqXg008/9dm/f79PUlKSrqCg4Hz35Y4dO6Z69tlnawEgMTGxfdSoUZaCggJlTk6Od3Z2dp1cbj+Xq9XqjnF1Hn744ejly5fXLF26tMfLPj1dsunOMapwr8u4umTTm+zs7Gvd25D0d90b1a+EhDE2C8B/AZAC+CPnfFO3+c8BeBKADUAdgH/nnJff5lgJIYQMgN5qMu6k++67z3js2DFVUVGRe0pKiikyMtKydetWtUqlErKysuoB+0l3+fLl1StXruxx3J2+xmhTKpUcAGQyGbfZbP1qmahWq4Xs7OzG7OzsxvT09OgDBw6oAgICugzY19N+HYmCy5kpKSnG/fv3+zz99NONzlqQm+Xv7y+6u7uLhYWFbjqdztL3GkNLn0fPGJMCeBvAbAA6AI8xxnTdFssDkMw5HwfgYwCbb3eghBBC7m5paWnGb7/91tfX11eQyWRQq9WCwWCQ5uXlqdLT01sBYPbs2YZdu3YFNDc3SwDg0qVL8srKyi4/rqdOnWrcu3evnyAIqKiokJ08edKrr317eXkJLS0tUlfz9u3b59XS0iIB7JdFysvLFRERERYfHx+hc8PbKVOmGHfv3u0PAGfPnlVUV1e7jRs3rn3GjBmGbdu2BVqtVgDocsnmtddeq/L397dlZmaOvuECc2H58uXV2dnZYxobGyUA0NjYKHn99ddvuW3KQOhPOpYKoJRz/j3n3ALgQwA/6bwA5/wg59xZBXQCQI/DIxNCCCGupKammpqammTJyckdl1+0Wq1JpVIJzksWc+fONTzyyCONKSkpWo1Go5szZ05UU1NTl0Ri0aJF+uDgYItGo4nPysoak5CQ0Orr6yt0319nGRkZTV9++aWvq0atp0+f9hg/fnycRqPRpaamxmVmZtanpaW1paammmQyGY+NjdVt2LAhaNWqVbWCIDCNRqObP39+1Pbt2y+7u7vzFStW1IWGhlq0Wm18bGysbseOHf6dt79jx44Ks9ksyc7OdnnuTEtL02i1Wp1Wq9XNmTMnvLfjWLVqVd20adMMEyZM0MXExMRPnjxZ6+HhITrnL1y4MNK5rUmTJml629ZAY31VbTHG5gGYxTl/0vE8E8C9nPNlPSz/FoAazvl/upi3BMASABg9enRSefmNX9XZc/oKVv9vAY69MB2jfN1veH1CCBnOGGO5nPPk273d/Pz8ywkJCT1eBhlumpubJT4+PmJNTY00JSUl7ujRo0U04u/gy8/PD0hISAh3Na8/bUhcXV9zmcUwxh4HkAwgzdV8zvm7AN4FgOTk5Fvq2oz6RSOEENKTmTNnxhgMBqnVamUrV66spmRk6OtPQnIVQFin56EAqrovxBibAeAlAGmc8x5vObpVzGV+RAghhPzg1KlTxYMdA7kx/WlDchpADGMsgjHmBuBRAPs6L8AYSwSwHcDDnHOX90cTQggZ0kRRFOkXH7ljHO8vsaf5fSYknHMbgGUA/gbgAoCPOOfnGWMvM8Yediz2GgAVgL8yxr5jjO3rYXOEEEKGpnN1dXU+lJSQO0EURVZXV+cD4FxPy/SrHxLO+VcAvuo2bW2nxzNuNkhCCCGDz2azPVlTU/PHmpqasaBhRcjtJwI4Z7PZnuxpAeqplRBCCJKSkmoBPNzngoTcIZQFE0IIIWTQUUJCCCGEkEE3bBOSvjp0I4QQQsjwMfwSEmr/TQghhNx1hl9CQgghhJC7DiUkhBBCCBl0lJAQcge0nqpBe1nTYIdBCCHDBiUkhNwB+k9KUP+HgsEOgxBChg1KSAjpJ6HVCm7rcRgGQgght4ASEnLTmtus2PD5eZhtQpfp3CZCtAg9rDV8Vf/mBBp2XxjsMAgh5K40bBMS6oakK1t9PQRjK/T6E7DZWm779jnn2JdfBZvwQw3BaweK8Oejl/FZXlWXZa9t/Qeq1h677TEAgGAww9bcjmubXoW5pOSO7MOprK0df280APih35v2okYAwL7aJhS3tl+3DhdEtOXXAQD0zIjzBw+isrISB9auxaX5/wZutSdq5u+/h9jaekfjJ4SQ4WTYJSR9dUPCOQcXhFvuOI1bLLBarWhtagIXuv7aFwUB1rY2AMCunTvx9v9+hve/OI5WvR7Nzc24WlGBL7/80h6L1QpusYCLIjjnEFtbIZpMXfclih1/osVy/fFYrb3HKgi4OGUqCqcm4XReJs6cfAqCICA3NxeiY5ucc7To9fjTjh1oaWnB3195E4XHv8Of9p1GS7sV5yqbr9uupbwcF++bBMvVSnycexW/+CAP//VNES5du4YL1y4g72wuAKDZcBUnL5TDJohos9hgqW+FFQLOHi+Bpb2tX+UtiBwWx+WQioMHsX79epS4SDiqXzmFmt+exv6vT+A/f/l7iG1tsF67BmtVFeq3bUfDjj/ZXyOLBdaqquvWb2xshCha0Xw8B9bq6l5jmnyyCI/mf4/wQ/k4tCMXZnAUektQuTsPS85fRtqpouvWafn7VTR+YJ/+F7dcvHk8D9O/u4RvlB6QRTyNK2v+D3V/LMD3//owKp5ZBsFo6UhSOuOiiOo1a2A6d75jWtV/nkDzgcs4XX36pt/fXBBguVoJAKi32GARB/cSVGNrG1raTddNFzlHfkvv7x2xrQ1C0603HDaXlMBaVYWrLxyB/tObS3I557A2X/8Z6mxnZb3LJJYQYscGq8fT5ORkfubMmRte72///Reo/vLfKIiKgKeXCVwhg2BWoE2igqQZkPrZUB3xAL5XXYStTYWxTRWQsJGoUxnAbV6QWQ1QNACXbX6YUlWGOr8AmNptiDPrITO2QD/SHQ3evqjCCFj8ZVAwCxJGHYd3QANqLGqcuzQBJww6zPLJBfMQEOpdhi3n/x2MA7PDTqGsxR8Xm6IxacR5SMxKuFvbENLcghapAnBTQO1zEdfkwfCrN8FrdC2a2vwQ0laFSywUrEGKEYIXWlUmSARfpEkT8AVrRJG0HW6twIOtF3FcEQh/K8NFfxXCVdcwssGKNkGOWg8PSL1t+Mx3MgKbKxFvqYKvvAklbaEY6dGA8VHHcLwmFT7yNphbFTh8LfW6so3xrkZlqy8UEgHRimokma9CFnENZyTx+K4sBu1WZb9eI7nMAqvNreO5h1sb1B51uNQ0pmPaJLEOxySBeMi9EBZ3Aw42pkCAFAAQHliBuuYRYIIUU0wlqOQj8EB7Ozz8lWj2a0KlLQR7m7wBAIlejchr8UeSSo86G0eAIKJa8MG/slqc5CpUcjmmjLyCoGvl+M4zBFdtIYgOuoj/K78XsawGP7l3D6qvhcJ0+T58KQQigZUiSGzF/UFVWDZqEbiHFNJr7ZBdNnY5RiFAASFchRHVjaiLCoKvxYT7qs8hyeiDjXExUPxfbY/lowhmUHgKcGtug1r0QONINxiYBHp4ABIGZhYh85Fh9DUjqpos8BttgbbFCIMpELXtFtQ0S6AeY8N0QzNavEohq/JAXEsLzsj9UOvth5GeRigtFjDRgHL5OHCpEdxXCvdmYOJ3+ahJHAOVQY+3Jv4YYEBkYyUmFF5Bg1wBlbkecrMc5fJAxOuvQskM+Ef0PRC4CSG1RiRK22G9WoajoePhq2hH2LVKqExGhNbUQYAMJzTxSKytw6mJCYiuvIpGgwU7fjwPY0+fQ1W7F3Tu9XiwphKlTe24HBCKk55RAIBneCU+U4RBJ6vEfUYj/qyJwcWQkXg6/zJCLp/El4mJqPDxBZgUollEg58PJuaeR0htJYJ4Ow753oNi7oNwDwMW2ypQUduKAKknWm01UFlE/D1Oi0damrCVhaPC4oUHDUW4JPFBiSrY/poESHAPE9EgWtHQKsU8qRmhUhuOeOpRYvGErU0KHx9PePiZYbxghRQcRfDHbFk7vrb98LlI9K4FGs0o8hiJf+Wl8G6owruz5kOQSACBg1k5Amxt+DYuAEEJ9/Tr89QdYyyXc558UysTMoQNu4TkpfXr8Jf260+mhBAyXIREmXD0qXk3tS4lJORuNewu2VzyGjHYIRBCBpjgrxjsEG6r8TJL3wsR8k9GNtgB3CjO7K1InjUewnyZFW2nTgESCYT6+o5lmoOkEFtk8JRbIV/wFPR7PoJ7ezvcTSaM2vI6Gt79A8zFxZB4e6Pd2Aa5aIN61Sq0fPMNTHl54DJ7rRGzMXAATC4HrFbYpFKMXPYMrn76KVraTPDT63E1ciQKHorAZb0W91ZeQlRJGUrdfeGvb0BISz08bTbYBAESUQQDwB3H4DNzJjySk3HtlVfAZRyiHJCa7MdmlUqgfvtNqJQeaPyf99D+/feQenpCqYuDaGxFy9/+hoDnfwllehpqVq0Ga2mB+WolGr2DkJn+SwDAZ/teQLOHCtfc/TBGMwqhMx9A43/+Bg2eAQhqrsOu5Cx4qpXwDm7CI0mT0Xq1EtJ/5EE+JhKG//0IkMkhY4C1rgH+WVn4/ekKvD9mOkbyFmS2/gOvqdKwNvgdWMNMKKzVYd+lB7HNdg4Nogc8A+rQ4GnBX+riMFHxPab7u6HS7wi8q2YgQpUIxWglgkZ5w1xYAK4JgPTb73BE2QYrF+FpCoTQdA25HgrMDh2FsWPH4nJuISLjwgEA1W1u8Ncz7Dqdj5MyFZZNjwbfvxeBhbnwW/xLtI4dh7AID+j/9Cf4Pf44Wisq4eahhNvIkbC2tWH/2csYee17eP7321DPy4AsMBBuEeFwH58IicoL5uIiQLCBubvjQLMJSzq1O11T0IrEBgv2KiXw8q2El6EO880WXP76KwT85jcICQyBIkqNa/uO4dmy71FgGgmLKMWLUMDPw4h8z3ZcabmGx2QJEJrKkeReD++fPY6r+YUInxKHopormGVyw/SmBvyq1h1eU4Pw/z79BBmPzkPNhQtwb2hCteCG+Rc98NenJyIlYgREsxlCfT0sFVfRqNfDkJAIb6UCUCoRKpdC/8EHUM39CQSpCHd3P5iKy2D4OhdBv5gHMAbRbEb9/+yEat48mEQRoiDAPygQXBQglUrBGAPnFlgsDVAqR8EsipAxBilj4JyDsZ5bdXHOcaW2HurGesi1WghXr0I0maCIjgGTMLQLAh7d/CxGjfDHWwvXw3jiAjzGBkMWEACx3QbmJgWT3OTgVY42W+aSErhFRECisCc0XBTBBQHWq1fBfHzwi68uYWK0FBmhQWjMr0VRkxkF/t5YGqeGW5A7xHor6i5WwSs5GB4mBomHAtzaggY3FZjNhkA/TzDG8OayNWgxViFzyn3wSEhEwZJnETNjCgKf+TncwsM7wmq02uAllUJ+s8dFyF1s2F2yefR3b+FEbQSWRdfgl08+cQcic82m16O9sBCqyZOvmycIAgwGA/z8/AYsnu5MZ8/C4uGON9e8CF+VEsv+sPuO77PwwmpUV3+MOO1vMWrUzzqmc5sIbhUhcb+5fJdzDkEQIJMNbr7MOcdntU0oazPj4SBfRHso0LT3a3jPng6psvf2NBdf+Ba7FEdhgRSvPL8K3GZF9eZccHAE/fs4KDWD914ZSsyCGVImhUwy7H4bdcGtVgjNzZAFBAAATPn5UGg0kLi73/Z90SUbcrcadt8CzhoSmXRgf2HI/PxcJiMAIJVKBzUZAQD3ceMgs5jhLrYjccZPBmSfzHHFj6NrUstkEjDZzV8NZIwNejLijOOn6q6vq9+ch/q1rgcUiBIDIIQrIfV2A7dKwcCgujeYkpFOFNK741IMk8s7khEAcE9IGMRoCBmeBv9b/waJjht/pWzYNX+54+RuCiz/y15IpNIB2V9AQDqqqj+Ct9fN3S1wt5thHYfQJ6YCAJhcilEbJoHJ6X1LCCGuDLuExPlbXErf6y5JB7BmITDwQdyfVgjpXfIr906TKAYmUSSEkOFo2J3WuaOGREI1JEMCJSOEEEJuh2F3VueOpiO9te4nhBBCyPAyDC/Z2HMoOV2zIUNY0H9MABvghteEEDKcDcOExE4Kuh5Phi63YM/BDoEQQoaVYVfN0NGGhH59EkIIIXeN4ZeQONqOSLv1dOiqgzfOeY/T+7WvTst131bn56JofyyK3OX63fd3I53R9XQMrpYx24Rety+K18dotgkd63P+w4i7/YlBEDnOXG7ssVw6/3dVNs5y676+q/33R3+XswniDb0G3WOvaW5Hs8nqcp5Tq9mGxlYLWtqtHcfY3GbtKG+rIPa4bvftADdfBoPV8aEg9v2+7U68iXUGUm+fRYtNRK3h+pF8+/P5JYTYDbtLNs7T5d78Gqz/7msw2IcqtwoccketiSByKGRSWAQRgmifLogcIgeUcgmsAocgcrhJJbAIIqQSBgn7oaGs1PHfZBXg6SaFTeQwO07UHm5StFsF9HQukTBAJpFAIZOg1WKDQiaF2eZ6eQ83KayO/YsiAOb8ArN3/CZyx7DmAu/YtnM7Skd/FpzDfnLr5TtPKrF39W0RREgY4CaTQCaRwOg42QGATMJgc2xEIZPAbBPh5minI3B7eQH2daWMQSZhaOm0vqebFO2268u7MzeZxN4Lq6t5jtei+2Nn3tk9AXUeu1wqgclqT8QYs0+TSexd/ksl9jg85PbLeyK379e5vFTCIJUwyB3LO7fp7OiNc0Apl3YkH90596eQSToeSyX2sjG021yu051C9sPr6DxmqYSBAR2vhyvO14HD/n6RMAbBkVDKJAwSR3lZbCKUcklHuVgEEYyxjnKVMAbn+dJ53M7XmoF1HBdj9sul/Umi4IidsR8+S1JHGTvL2vla8G7/3aT2snS2WXd+LuxjOHT7D8djxz+z49ilEnvczlCdnxvnPgB7WUiYfVnnZ1DKGETOO+LjvOv7wSpw+/tFal/XKoiwiRwyCev4jDpfU7NN7PjvnCaTMFhFDiljWPdjHR5NHd2vsiTkn8WwS0ic30B+HgpkJYR3fCEJgv1rw83xBW+2iiiqMWDMCE94K2Uw20TYRBHucmnHF739BGn/UnbicP5yt5/oLYKIeqMFFpuIxlYL7o3wdyQoAs5XGRCr9kKd0YxgH3eUXGvBvZH+aDULYAxoMwtQyCXwVMhgsYkwttuglEvw3vFy/Cw5FN5KOTjsJwA3mQQSxxeiINovTEmlDKLIUddihp+nG1QKGVocJzq5lHWcKBhjsNhE5BTVQhA5xoX6IECl6PiyVcqlkEslsAoiTFYBKoUMgsjRZhHwwakr+Jd4NSIDVTBZBBwsrsWU6ABwACqFDFIJQ7tVwJdnq5EeGwTGAInjpNtmEfB1QTUykkIhYQweblL7L2MADUYLLIIItZcCf/y/S7gvcgTGhfpA4vjilziSwL8X1yFpjB9kUob6FgtK64xIDPOF0WyDyDlGeis7TordGdqtYGAoqzNipI8SXgoZ5FJJx3vAebIwWQUwMEgl9te63SqgxWxDoJcCnP9wknW+DRj7ISGwCiIOXaxDmJ8HfD3kqGoy4R9XmpAQ6oPE0X7wVEjRZhE6TnCCyGGyCvjLySsdcT6oU6O2xYzvKpoAAFOiA+yJkpsU0UEq+zmWASXXjAj2UcJdLu04hg9OXcGDupGoajYhMsAT7m4yHDhfg/FhvgjwUsBis9f2KOTSjuScMfsJ2f6r3YwQP/eOk7Gzh2POcV0y73z/yaUS+5hL3Lk9dCQCsn6OweIsE5lU0vF+dp60na+DMzFyfv6cMf/wWXQkTOBdEyfOIZFcn0h1HFenJAocHeVrT+DsnzfnMoL4Q7ItcZSb88cJYz8kbIzZkymLwO3JlGMbgmj/Hvn2wjWU1hoxNSYAumBvtFps8HCzf722tNvgrZTBIoj2suUcMWqvfpUjIf9Mht1YNg/91w4UVo/En2dKkf7ArDsQGSGEDF00lg25W/WrDQljbBZjrJgxVsoYe8HFfAVjbI9j/knGWPjtDrTTzgAAUvkwrNwhhBBCiEt9JiSMMSmAtwHMBqAD8BhjTNdtsScA6Dnn0QDeAPDq7Q7UiXPn4HqUkBBCCCF3i/7UkKQCKOWcf885twD4EED34WR/AuA9x+OPATzA7lBXqs6eWuWUkBBCCCF3jf4kJCEAKjo9v+qY5nIZzrkNQDOAEbcjwO6c/ZDIFW53YvOEEEIIGQT9SUhc1XR0bwnbn2XAGFvCGDvDGDtTV1fXn/iuM4q3wjvADF9vn5tanxBCCCFDT3+ue1wFENbpeSiAqh6WucoYkwHwAdDYfUOc83cBvAvY77K5mYD//B/P3MxqhBBCCBnC+lNDchpADGMsgjHmBuBRAPu6LbMPwCLH43kAcjh1T0gIIYSQfuqzhoRzbmOMLQPwNwBSAH/inJ9njL0M4AznfB+AHQB2McZKYa8ZefROBk0IIYSQu0u/blXhnH8F4Ktu09Z2etwO4JHbGxohhBBC/lkMu8H1CCGEEHL3oYSEEEIIIYOOEhJCCCGEDDpKSAghhBAy6CghIYQQQsigY4PVXQhjrA5A+U2uHgCg/jaGMxAo5oEx3GIebvECFPNA6SnmMZzzwIEOhpA7bdASklvBGDvDOU8e7DhuBMU8MIZbzMMtXoBiHijDMWZCbgVdsiGEEELIoKOEhBBCCCGDbrgmJO8OdgA3gWIeGMMt5uEWL0AxD5ThGDMhN21YtiEhhBBCyN1luNaQEEIIIeQuMuwSEsbYLMZYMWOslDH2wmDH48QYu8wYK2CMfccYO+OY5s8Y+4YxVuL47+eYzhhjv3ccw1nG2IQBivFPjLFaxti5TtNuOEbG2CLH8iWMsUWDEPN6xlilo6y/Y4w91Gnei46Yixlj/9Jp+oC8bxhjYYyxg4yxC4yx84yx/3BMH7Ll3EvMQ7mclYyxU4yxfEfMGxzTIxhjJx1ltocx5uaYrnA8L3XMD+/rWAYw5v9hjF3qVM7jHdMH/b1ByIDinA+bPwBSAGUAIgG4AcgHoBvsuByxXQYQ0G3aZgAvOB6/AOBVx+OHAHwNgAGYCODkAMU4DcAEAOduNkYA/gC+d/z3czz2G+CY1wP4pYtldY73hAJAhOO9Ih3I9w2AYAATHI+9AFx0xDVky7mXmIdyOTMAKsdjOYCTjvL7CMCjjunbACx1PP45gG2Ox48C2NPbsQxwzP8DYJ6L5Qf9vUF/9DeQf8OthiQVQCnn/HvOuQXAhwB+Msgx9eYnAN5zPH4PwE87Td/J7U4A8GWMBd/pYDjnhwE03mKM/wLgG855I+dcD+AbALMGOOae/ATAh5xzM+f8EoBS2N8zA/a+4ZxXc87/4XjcAuACgBAM4XLuJeaeDIVy5pxzo+Op3PHHAUwH8LFjevdydpb/xwAeYIyxXo5lIGPuyaC/NwgZSMMtIQkBUNHp+VX0/sU5kDiAA4yxXMbYEsc0Nee8GrB/6QMIckwfSsdxozEOldiXOaqx/+S8/IEhFrPjskAi7L+Eh0U5d4sZGMLlzBiTMsa+A1AL+0m5DEAT59zmYv8dsTnmNwMYMdgxc86d5bzRUc5vMMYU3WPuFttQ+QwSclsNt4SEuZg2VG4Tmsw5nwBgNoBnGGPTell2KB+HU08xDoXY3wEQBWA8gGoAWxzTh0zMjDEVgP8FsJxzbuhtURfThkrMQ7qcOecC53w8gFDYazXietn/kIyZMTYWwIsAtABSYL8Ms9qx+JCImZCBMtwSkqsAwjo9DwVQNUixdME5r3L8rwXwKexfkNecl2Ic/2sdiw+l47jRGAc9ds75NccXuwjgD/ihin1IxMwYk8N+Yv8L5/wTx+QhXc6uYh7q5ezEOW8C8HfY21n4MsZkLvbfEZtjvg/slwIHO+ZZjktmnHNuBvBnDNFyJuROG24JyWkAMY6W9G6wN07bN8gxgTHmyRjzcj4G8CCAc7DH5mwBvwjAZ47H+wAsdLSinwig2VmdPwhuNMa/AXiQMebnqMJ/0DFtwHRrbzMH9rJ2xvyo446KCAAxAE5hAN83jnYJOwBc4Jz/rtOsIVvOPcU8xMs5kDHm63jsDmAG7G1fDgKY51isezk7y38egBzOOe/lWAYq5qJOiSqDvc1L53Iekp9BQu6IwWpNe7N/sLc8vwj79eKXBjseR0yRsLfUzwdw3hkX7Neo/x+AEsd/f8d0BuBtxzEUAEgeoDg/gL3q3Qr7r6wnbiZGAP8Oe+O/UgBZgxDzLkdMZ2H/0g7utPxLjpiLAcwe6PcNgCmwV5+fBfCd4++hoVzOvcQ8lMt5HIA8R2znAKx1TI+EPaEoBfBXAArHdKXjealjfmRfxzKAMec4yvkcgN344U6cQX9v0B/9DeQf9dRKCCGEkEE33C7ZEEIIIeQuRAkJIYQQQgYdJSSEEEIIGXSUkBBCCCFk0FFCQgghhJBBRwkJIYQQQgYdJSSEEEIIGXSUkBBCCCHYTMNSAAAACElEQVRk0P1/LEvwD9GMA9EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a28b27ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start Episode 1\n",
      "Episode: 1\n",
      "End of test PF value: 41523.0\n",
      "Min of test PF value: 9688.0\n",
      "Max of test PF value: 42800.0\n",
      "Mean of test PF value: 22556.0\n",
      "Max Draw Down of test PF value: 5223.0\n",
      "End of test weights: [0.0111495  0.09926072 0.09917854 0.10085267 0.09917729 0.09822252\n",
      " 0.09868288 0.09850323 0.09902604 0.09723976 0.09870685]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2b162c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a28aff588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a28d9ae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "############# TRAINING #####################\n",
    "###########################################\n",
    "tf.reset_default_graph()\n",
    "\n",
    "# sess\n",
    "sess = tf.Session()\n",
    "\n",
    "# initialize networks\n",
    "actor = Policy(m, n, sess, optimizer,\n",
    "                 trading_cost=trading_cost, \n",
    "                 interest_rate=interest_rate)  # policy initialization\n",
    "\n",
    "# initialize tensorflow graphs\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "\n",
    "\n",
    "list_final_pf = list()\n",
    "list_final_pf_eq = list()\n",
    "list_final_pf_s = list()\n",
    "\n",
    "list_final_pf_fu = list()\n",
    "state_fu = [0]*m\n",
    "done_fu = [0]*m\n",
    "\n",
    "pf_value_t_fu = [0]*m\n",
    "\n",
    "for i in range(m):\n",
    "    list_final_pf_fu.append(list())\n",
    "    \n",
    "\n",
    "###### Train #####\n",
    "for e in range(n_episodes):\n",
    "    print('Start Episode', e)\n",
    "    if e==0:\n",
    "        eval_perf('Before Training')\n",
    "    print('Episode:', e)\n",
    "    #init the PVM with the training parameters\n",
    "    memory = PVM(m,sample_bias, total_steps = total_steps_train, \n",
    "                 batch_size = batch_size, w_init = w_init_train)\n",
    "    \n",
    "    for nb in range(n_batches):\n",
    "        #draw the starting point of the batch \n",
    "        i_start = memory.draw()\n",
    "        \n",
    "        \n",
    "        #reset the environment with the weight from PVM at the starting point\n",
    "        #reset also with a portfolio value with initial portfolio value\n",
    "        state, done = env.reset(memory.get_W(i_start), pf_init_train, t=i_start )\n",
    "        state_eq, done_eq = env_eq.reset(w_eq, pf_init_train, t=i_start )\n",
    "        state_s, done_s = env_s.reset(w_s, pf_init_train, t=i_start )\n",
    "        \n",
    "        for i in range(m):\n",
    "            state_fu[i], done_fu[i] = env_fu[i].reset(action_fu[i], pf_init_train, t=i_start )\n",
    "        \n",
    "        \n",
    "        \n",
    "        list_X_t, list_W_previous, list_pf_value_previous, list_dailyReturn_t = [], [], [], []\n",
    "        list_pf_value_previous_eq, list_pf_value_previous_s = [],[]\n",
    "        list_pf_value_previous_fu = list()\n",
    "        for i in range(m):\n",
    "            list_pf_value_previous_fu.append(list())\n",
    "            \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        for bs in range(batch_size):\n",
    "            \n",
    "            #load the different inputs from the previous loaded state \n",
    "            X_t = state[0].reshape([-1] + list(state[0].shape))\n",
    "            W_previous = state[1].reshape([-1] + list(state[1].shape))\n",
    "            pf_value_previous = state[2]\n",
    "            \n",
    "            \n",
    "            if np.random.rand()< ratio_greedy:\n",
    "                #print('go')\n",
    "                #computation of the action of the agent\n",
    "                action = actor.compute_W(X_t, W_previous)\n",
    "            else:\n",
    "                action = get_random_action(m)\n",
    "            \n",
    "            #given the state and the action, call the environment to go one time step later \n",
    "            state, reward, done = env.step(action)\n",
    "            state_eq, reward_eq, done_eq = env_eq.step(w_eq)\n",
    "            state_s, reward_s, done_s = env_s.step(w_s)\n",
    "            \n",
    "            for i in range(m):\n",
    "                state_fu[i], _ , done_fu[i] = env_fu[i].step(action_fu[i])\n",
    "\n",
    "            \n",
    "            \n",
    "            #get the new state \n",
    "            X_next = state[0]\n",
    "            W_t = state[1]\n",
    "            pf_value_t = state[2]\n",
    "            \n",
    "            pf_value_t_eq = state_eq[2]\n",
    "            pf_value_t_s = state_s[2]\n",
    "            \n",
    "            for i in range(m):\n",
    "                pf_value_t_fu[i] = state_fu[i][2]\n",
    "                \n",
    "            \n",
    "            #let us compute the returns \n",
    "            dailyReturn_t = X_next[-1, :, -1]\n",
    "            #update into the PVM\n",
    "            memory.update(i_start+bs, W_t)\n",
    "            #store elements\n",
    "            list_X_t.append(X_t.reshape(state[0].shape))\n",
    "            list_W_previous.append(W_previous.reshape(state[1].shape))\n",
    "            list_pf_value_previous.append([pf_value_previous])\n",
    "            list_dailyReturn_t.append(dailyReturn_t)\n",
    "            \n",
    "            list_pf_value_previous_eq.append(pf_value_t_eq)\n",
    "            list_pf_value_previous_s.append(pf_value_t_s)\n",
    "            \n",
    "            for i in range(m):\n",
    "                list_pf_value_previous_fu[i].append(pf_value_t_fu[i])\n",
    "            \n",
    "            if bs==batch_size-1:\n",
    "                list_final_pf.append(pf_value_t)\n",
    "                list_final_pf_eq.append(pf_value_t_eq)\n",
    "                list_final_pf_s.append(pf_value_t_s)\n",
    "                for i in range(m):\n",
    "                    list_final_pf_fu[i].append(pf_value_t_fu[i])\n",
    "\n",
    "            \n",
    "            \n",
    "#             #printing\n",
    "#             if bs==0:\n",
    "#                 print('start', i_start)\n",
    "#                 print('PF_start', round(pf_value_previous,0))\n",
    "\n",
    "#             if bs==batch_size-1:\n",
    "#                 print('PF_end', round(pf_value_t,0))\n",
    "#                 print('weight', W_t)\n",
    "\n",
    "        list_X_t = np.array(list_X_t)\n",
    "        list_W_previous = np.array(list_W_previous)\n",
    "        list_pf_value_previous = np.array(list_pf_value_previous)\n",
    "        list_dailyReturn_t = np.array(list_dailyReturn_t)\n",
    "        \n",
    "        \n",
    "        #for each batch, train the network to maximize the reward\n",
    "        actor.train(list_X_t, list_W_previous,\n",
    "                    list_pf_value_previous, list_dailyReturn_t)\n",
    "    eval_perf(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-08T03:24:22.473379Z",
     "start_time": "2018-07-08T03:22:45.265967Z"
    },
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "PZpZCKcwCCK4",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current portfolio value 9993.0\n",
      "weights [[0.0062508  0.09846592 0.09850711 0.10444584 0.09894872 0.09924217\n",
      "  0.09801345 0.09904172 0.09938086 0.09867361 0.0990298 ]]\n",
      "current portfolio value 9934.0\n",
      "weights [[0.01120021 0.0989992  0.09938443 0.09901933 0.09898423 0.09875402\n",
      "  0.09882854 0.09857667 0.09865061 0.09902017 0.09858258]]\n",
      "current portfolio value 9915.0\n",
      "weights [[0.01120294 0.0988903  0.09869209 0.09903787 0.09903787 0.0986621\n",
      "  0.09903787 0.09903787 0.09903813 0.09855022 0.09881274]]\n",
      "current portfolio value 9933.0\n",
      "weights [[0.0111087  0.09837    0.09840221 0.10416636 0.09848968 0.09846496\n",
      "  0.09857186 0.09745443 0.09851925 0.09817885 0.09827371]]\n",
      "current portfolio value 9967.0\n",
      "weights [[0.01113317 0.09852288 0.09831241 0.09871878 0.09879521 0.09888554\n",
      "  0.09832548 0.10059552 0.09831937 0.09900841 0.09938323]]\n"
     ]
    }
   ],
   "source": [
    "#######TEST#######\n",
    "\n",
    "\n",
    "#initialization of the environment \n",
    "state, done = env.reset(w_init_test, pf_init_test, t = total_steps_train)\n",
    "\n",
    "state_eq, done_eq = env_eq.reset(w_eq, pf_init_test, t = total_steps_train)\n",
    "state_s, done_s = env_s.reset(w_s, pf_init_test, t = total_steps_train)\n",
    "\n",
    "for i in range(m):\n",
    "    state_fu[i],  done_fu[i] = env_fu[i].reset(action_fu[i], pf_init_test, t = total_steps_train)\n",
    "\n",
    "\n",
    "#first element of the weight and portfolio value \n",
    "p_list = [pf_init_test]\n",
    "w_list = [w_init_test]\n",
    "\n",
    "p_list_eq = [pf_init_test]\n",
    "p_list_s = [pf_init_test]\n",
    "\n",
    "\n",
    "p_list_fu = list()\n",
    "for i in range(m):\n",
    "    p_list_fu.append([pf_init_test])\n",
    "    \n",
    "pf_value_t_fu = [0]*m\n",
    "    \n",
    "\n",
    "for k in range(total_steps_train +total_steps_val-int(n/2), total_steps_train +total_steps_val +total_steps_test -n):\n",
    "    X_t = state[0].reshape([-1]+ list(state[0].shape))\n",
    "    W_previous = state[1].reshape([-1]+ list(state[1].shape))\n",
    "    pf_value_previous = state[2]\n",
    "    #compute the action \n",
    "    action = actor.compute_W(X_t, W_previous)\n",
    "    #step forward environment \n",
    "    state, reward, done = env.step(action)\n",
    "    state_eq, reward_eq, done_eq = env_eq.step(w_eq)\n",
    "    state_s, reward_s, done_s = env_s.step(w_s)\n",
    "    \n",
    "    \n",
    "    for i in range(m):\n",
    "        state_fu[i], _ , done_fu[i] = env_fu[i].step(action_fu[i])\n",
    "    \n",
    "    \n",
    "    X_next = state[0]\n",
    "    W_t = state[1]\n",
    "    pf_value_t = state[2]\n",
    "    \n",
    "    pf_value_t_eq = state_eq[2]\n",
    "    pf_value_t_s = state_s[2]\n",
    "    for i in range(m):\n",
    "        pf_value_t_fu[i] = state_fu[i][2]\n",
    "    \n",
    "    dailyReturn_t = X_next[-1, :, -1]\n",
    "    if k%20 == 0:\n",
    "        print('current portfolio value', round(pf_value_previous,0))\n",
    "        print('weights', W_previous)\n",
    "    p_list.append(pf_value_t)\n",
    "    w_list.append(W_t)\n",
    "    \n",
    "    p_list_eq.append(pf_value_t_eq)\n",
    "    p_list_s.append(pf_value_t_s)\n",
    "    for i in range(m):\n",
    "        p_list_fu[i].append(pf_value_t_fu[i])\n",
    "        \n",
    "    #here to breack the loop/not in original code     \n",
    "    if k== total_steps_train +total_steps_val-int(n/2) + 100:\n",
    "        break\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-08T14:16:10.728927Z",
     "start_time": "2018-07-08T14:16:10.671401Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "path = \"individual_stocks_5yr/\"\n",
    "times = pd.read_csv(path+\"A_data.csv\").date\n",
    "test_start_day =total_steps_train +total_steps_val-int(n/2)+10\n",
    "times = list(times[test_start_day:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-08T14:16:12.074157Z",
     "start_time": "2018-07-08T14:16:11.667043Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2cd2d710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#batch_size, learning, ratio_greedy, e, n, kernel1_size, n_batches, ratio_regul\n",
    "\n",
    "data_type = path_data.split('/')[2][5:].split('.')[0]\n",
    "namesBio=['JNJ','PFE','AMGN','MDT','CELG','LLY']\n",
    "namesUtilities=['XOM','CVX','MRK','SLB','MMM']\n",
    "namesTech=['FB','AMZN','MSFT','AAPL','T','VZ','CMCSA','IBM','CRM','INTC']\n",
    "\n",
    "\n",
    "if data_type == 'Utilities':\n",
    "    list_stock = namesUtilities\n",
    "elif data_type == 'Bio':\n",
    "    list_stock = namesBio\n",
    "elif data_type == 'Tech':\n",
    "    list_stock = namesTech\n",
    "else:\n",
    "    list_stock = [i for i in range(m)]\n",
    "\n",
    "\n",
    "plt.title('Portfolio Value (Test Set) {}: {}, {}, {}, {}, {}, {}, {}, {}'.format(data_type, batch_size, learning, ratio_greedy, e, n, kernel1_size, n_batches, ratio_regul))\n",
    "plt.plot(p_list, label = 'Agent Portfolio Value')\n",
    "plt.plot(p_list_eq, label = 'Equi-weighted Portfolio Value')\n",
    "plt.plot(p_list_s, label = 'Secured Portfolio Value')\n",
    "for i in range(m):\n",
    "    plt.plot(p_list_fu[i], label = 'Full Stock {} Portfolio Value'.format(list_stock[i]))\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-08T14:16:13.859470Z",
     "start_time": "2018-07-08T14:16:13.563458Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2cdaf1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names = ['Money'] + list_stock\n",
    "w_list = np.array(w_list)\n",
    "for j in range(m+1):\n",
    "    plt.plot(w_list[:,j], label = 'Weight Stock {}'.format(names[j]))\n",
    "    plt.title('Weight evolution during testing')\n",
    "    plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-08T14:16:19.492492Z",
     "start_time": "2018-07-08T14:16:19.284505Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a2cf7c160>]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2ca06438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.array(p_list)-np.array(p_list_eq))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-08T14:16:22.195478Z",
     "start_time": "2018-07-08T14:16:22.013562Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2d0d9eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index1=0\n",
    "index2=-1\n",
    "\n",
    "plt.plot(list_final_pf[index1:index2], label = 'Agent Portfolio Value')\n",
    "plt.plot(list_final_pf_eq[index1:index2], label = 'Baseline Portfolio Value')\n",
    "plt.plot(list_final_pf_s[index1:index2], label = 'Secured Portfolio Value')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-08T14:16:23.038728Z",
     "start_time": "2018-07-08T14:16:22.867818Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a2d338c50>]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2d0fe278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot((np.array(list_final_pf)-np.array(list_final_pf_eq)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T18:54:39.717210Z",
     "start_time": "2018-05-05T18:54:39.712361Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "default_view": {},
   "name": "DPM_v2.ipynb",
   "provenance": [],
   "version": "0.3.2",
   "views": {}
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "219px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "position": {
    "height": "696px",
    "left": "611.992px",
    "right": "20px",
    "top": "113.984px",
    "width": "800px"
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
